.. Copyright (C) 2001-2012 NLTK Project
.. For license information, see LICENSE.TXT

================
 Corpus Readers
================

The `nltk.corpus` package defines a collection of *corpus reader*
classes, which can be used to access the contents of a diverse set of
corpora.  The list of available corpora is given at:

http://nltk.googlecode.com/svn/trunk/nltk_data/index.xml

Each corpus reader class is specialized to handle a specific
corpus format.  In addition, the `nltk.corpus` package automatically
creates a set of corpus reader instances that can be used to access
the corpora in the NLTK data package.
Section `Corpus Reader Objects`_ ("Corpus Reader Objects") describes
the corpus reader instances that can be used to read the corpora in
the NLTK data package.  Section `Corpus Reader Classes`_ ("Corpus
Reader Classes") describes the corpus reader classes themselves, and
discusses the issues involved in creating new corpus reader objects
and new corpus reader classes.  Section `Regression Tests`_
("Regression Tests") contains regression tests for the corpus readers
and associated functions and classes.

.. contents:: **Table of Contents**
  :depth: 2
  :backlinks: none

---------------------
Corpus Reader Objects
---------------------

Overview
========

NLTK includes a diverse set of corpora which can be
read using the ``nltk.corpus`` package.  Each corpus is accessed by
means of a "corpus reader" object from ``nltk.corpus``:

    >>> import nltk.corpus
    >>> # The Brown corpus:
    >>> nltk.corpus.brown
    <CategorizedTaggedCorpusReader in '.../corpora/brown'...>
    >>> # The Penn Treebank Corpus:
    >>> nltk.corpus.treebank
    <BracketParseCorpusReader in '.../corpora/treebank/combined'...>
    >>> # The Name Genders Corpus:
    >>> nltk.corpus.names
    <WordListCorpusReader in '.../corpora/names'...>
    >>> # The Inaugural Address Corpus:
    >>> nltk.corpus.inaugural
    <PlaintextCorpusReader in '.../corpora/inaugural'...>

Most corpora consist of a set of files, each containing a document (or
other pieces of text).  A list of identifiers for these files is
accessed via the ``fileids()`` method of the corpus reader:

    >>> nltk.corpus.treebank.fileids() # doctest: +ELLIPSIS
    ['wsj_0001.mrg', 'wsj_0002.mrg', 'wsj_0003.mrg', 'wsj_0004.mrg', ...]
    >>> nltk.corpus.inaugural.fileids() # doctest: +ELLIPSIS
    ['1789-Washington.txt', '1793-Washington.txt', '1797-Adams.txt', ...]

Each corpus reader provides a variety of methods to read data from the
corpus, depending on the format of the corpus.  For example, plaintext
corpora support methods to read the corpus as raw text, a list of
words, a list of sentences, or a list of paragraphs.

    >>> from nltk.corpus import inaugural
    >>> inaugural.raw('1789-Washington.txt') # doctest: +ELLIPSIS
    'Fellow-Citizens of the Senate ...
    >>> inaugural.words('1789-Washington.txt')
    ['Fellow', '-', 'Citizens', 'of', 'the', 'Senate', ...]
    >>> inaugural.sents('1789-Washington.txt') # doctest: +ELLIPSIS
    [['Fellow', '-', 'Citizens'...], ['Among', 'the', 'vicissitudes'...]...]
    >>> inaugural.paras('1789-Washington.txt') # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
    [[['Fellow', '-', 'Citizens'...]],
     [['Among', 'the', 'vicissitudes'...],
      ['On', 'the', 'one', 'hand', ',', 'I'...]...]...]

Each of these reader methods may be given a single document's item
name or a list of document item names.  When given a list of document
item names, the reader methods will concatenate together the contents
of the individual documents.

    >>> l1 = len(inaugural.words('1789-Washington.txt'))
    >>> l2 = len(inaugural.words('1793-Washington.txt'))
    >>> l3 = len(inaugural.words(['1789-Washington.txt', '1793-Washington.txt']))
    >>> print '%s+%s == %s' % (l1, l2, l3)
    1538+147 == 1685

If the reader methods are called without any arguments, they will
typically load all documents in the corpus.

    >>> len(inaugural.words())
    145735

If a corpus contains a README file, it can be accessed with a ``readme()`` method:

    >>> inaugural.readme()[:32]
    'C-Span Inaugural Address Corpus\n'

Plaintext Corpora
=================

Here are the first few words from each of NLTK's plaintext corpora:

    >>> nltk.corpus.abc.words()
    ['PM', 'denies', 'knowledge', 'of', 'AWB', 'kickbacks', ...]
    >>> nltk.corpus.genesis.words()
    [u'In', u'the', u'beginning', u'God', u'created', ...]
    >>> nltk.corpus.gutenberg.words(fileids='austen-emma.txt')
    ['[', 'Emma', 'by', 'Jane', 'Austen', '1816', ']', ...]
    >>> nltk.corpus.inaugural.words()
    ['Fellow', '-', 'Citizens', 'of', 'the', 'Senate', ...]
    >>> nltk.corpus.state_union.words()
    ['PRESIDENT', 'HARRY', 'S', '.', 'TRUMAN', "'", 'S', ...]
    >>> nltk.corpus.udhr.words() # doctest: +NORMALIZE_WHITESPACE
    ['\xc0\xf3\xe0\xfe\xfb\xf2\xfa\xfb\xfe\xf1\xe0',
     '\xe8\xe7\xe8\xed6\xfa\xe0', ...]
    >>> nltk.corpus.webtext.words()
    ['Cookie', 'Manager', ':', '"', 'Don', "'", 't', ...]

We can obtain character offsets for tokens in plaintext corpora
using the ``sourced`` flag:

    >>> nltk.corpus.inaugural.words(sourced=True)
    ['Fellow'@[0:6], '-'@[6], 'Citizens'@[7:15], ...]

Tagged Corpora
==============

In addition to the plaintext corpora, NLTK's data package also
contains a wide variety of annotated corpora.  For example, the Brown
Corpus is annotated with part-of-speech tags, and defines additional
methods ``tagged_*()`` which words as `(word,tag)` tuples, rather
than just bare word strings.

    >>> from nltk.corpus import brown
    >>> print brown.words()
    ['The', 'Fulton', 'County', 'Grand', 'Jury', 'said', ...]
    >>> print brown.tagged_words()
    [('The', 'AT'), ('Fulton', 'NP-TL'), ...]
    >>> print brown.sents() # doctest: +ELLIPSIS
    [['The', 'Fulton', 'County'...], ['The', 'jury', 'further'...], ...]
    >>> print brown.tagged_sents() # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
    [[('The', 'AT'), ('Fulton', 'NP-TL')...],
     [('The', 'AT'), ('jury', 'NN'), ('further', 'RBR')...]...]
    >>> print brown.paras(categories='reviews') # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
    [[['It', 'is', 'not', 'news', 'that', 'Nathan', 'Milstein'...],
      ['Certainly', 'not', 'in', 'Orchestra', 'Hall', 'where'...]],
     [['There', 'was', 'about', 'that', 'song', 'something', ...],
      ['Not', 'the', 'noblest', 'performance', 'we', 'have', ...], ...], ...]
    >>> print brown.tagged_paras(categories='reviews') # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
    [[[('It', 'PPS'), ('is', 'BEZ'), ('not', '*'), ...],
      [('Certainly', 'RB'), ('not', '*'), ('in', 'IN'), ...]],
     [[('There', 'EX'), ('was', 'BEDZ'), ('about', 'IN'), ...],
      [('Not', '*'), ('the', 'AT'), ('noblest', 'JJT'), ...], ...], ...]

Similarly, the Indian Langauge POS-Tagged Corpus includes samples of
Indian text annotated with part-of-speech tags:

    >>> from nltk.corpus import indian
    >>> print indian.words() # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
    ['\xe0\xa6\xae\xe0\xa6\xb9\xe0\xa6\xbf\...',
     '\xe0\xa6\xb8\xe0\xa6\xa8\xe0\xa7\x8d\xe0...', ...]
    >>> print indian.tagged_words() # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
    [('\xe0\xa6\xae\xe0\xa6\xb9\xe0\xa6\xbf...', 'NN'),
     ('\xe0\xa6\xb8\xe0\xa6\xa8\xe0\xa7\x8d\xe0...', 'NN'), ...]

Tagged corpora support access to simplified tags, e.g. where all nouns
tags are collapsed to a single category ``N``:

    >>> print brown.tagged_sents(simplify_tags=True) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
    [[('The', 'DET'), ('Fulton', 'NP'), ('County', 'N'), ('Grand', 'ADJ'), ...],
     [('The', 'DET'), ('jury', 'N'), ('further', 'ADV'), ('said', 'VD'), ...]...]
    >>> from nltk.corpus import conll2000, switchboard
    >>> print conll2000.tagged_words(simplify_tags=True) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
    [('Confidence', 'N'), ('in', 'P'), ('the', 'DET'), ...]
    >>> print switchboard.tagged_words(simplify_tags=True) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
    [('Uh', 'UH'), (',', ','), ('do', 'V'), ('you', 'PRO'), ...]

Use ``nltk.app.pos_concordance()`` to access a GUI for searching tagged corpora.

Chunked Corpora
===============

The CoNLL corpora also provide chunk structures, which are encoded as
flat trees.  The CoNLL 2000 Corpus includes phrasal chunks; and the
CoNLL 2002 Corpus includes named entity chunks.

    >>> from nltk.corpus import conll2000, conll2002
    >>> print conll2000.sents() # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
    [['Confidence', 'in', 'the', 'pound', 'is', 'widely', ...],
     ['Chancellor', 'of', 'the', 'Exchequer', ...], ...]
    >>> for tree in conll2000.chunked_sents()[:2]:
    ...     print tree # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
    (S
      (NP Confidence/NN)
      (PP in/IN)
      (NP the/DT pound/NN)
      (VP is/VBZ widely/RB expected/VBN to/TO take/VB)
      (NP another/DT sharp/JJ dive/NN)
      if/IN
      ...)
    (S
      Chancellor/NNP
      (PP of/IN)
      (NP the/DT Exchequer/NNP)
      ...)
    >>> print conll2002.sents() # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
    [[u'Sao', u'Paulo', u'(', u'Brasil', u')', u',', ...], [u'-'], ...]
    >>> for tree in conll2002.chunked_sents()[:2]:
    ...     print tree # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
    (S
      (LOC Sao/NC Paulo/VMI)
      (/Fpa
      (LOC Brasil/NC)
      )/Fpt
      ...)
    (S -/Fg)

.. note:: Since the CONLL corpora do not contain paragraph break
   information, these readers do not support the ``para()`` method.)

.. warning:: if you call the conll corpora reader methods without any
   arguments, they will return the contents of the entire corpus,
   *including* the 'test' portions of the corpus.)

SemCor is a subset of the Brown corpus tagged with WordNet senses and 
named entities. Both kinds of lexical items include multiword units, 
which are encoded as chunks (senses and part-of-speech tags pertain 
to the entire chunk).

    >>> from nltk.corpus import semcor
    >>> semcor.words()
    ['The', 'Fulton', 'County', 'Grand', 'Jury', 'said', ...]
    >>> semcor.chunks()
    [['The'], ['Fulton', 'County', 'Grand', 'Jury'], ['said'], ...]
    >>> semcor.sents() # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
    [['The', 'Fulton', 'County', 'Grand', 'Jury', 'said', ...], 
    ['The', 'jury', 'further', 'said', ...], ...]
    >>> semcor.chunk_sents() # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
    [[['The'], ['Fulton', 'County', 'Grand', 'Jury'], ['said'], ... 
    ['.']], [['The'], ['jury'], ['further'], ['said'], ... ['.']], ...]
    >>> list(map(str, semcor.tagged_chunks(tag='both')[:3]))
    ['(DT The)', '(group.01 (NE (NNP Fulton County Grand Jury)))', '(say.01 (VB said))']
    >>> [[str(c) for c in s] for s in semcor.tagged_sents(tag='both')[:2]] # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
    [['(DT The)', '(group.01 (NE (NNP Fulton County Grand Jury)))', '(say.01 (VB said))', ...
     '(None .)'], ['(DT The)', ... '(None .)']]

The IEER corpus is another chunked corpus.  This corpus is unusual in
that each corpus item contains multiple documents.  (This reflects the
fact that each corpus file contains multiple documents.)  The IEER
corpus defines the `parsed_docs` method, which returns the documents
in a given item as `IEERDocument` objects:

    >>> from nltk.corpus import ieer
    >>> ieer.fileids() # doctest: +NORMALIZE_WHITESPACE
    ['APW_19980314', 'APW_19980424', 'APW_19980429',
     'NYT_19980315', 'NYT_19980403', 'NYT_19980407']
    >>> docs = ieer.parsed_docs('APW_19980314')
    >>> print docs[0]
    <IEERDocument APW19980314.0391: 'Kenyans protest tax hikes'>
    >>> print docs[0].docno
    APW19980314.0391
    >>> print docs[0].doctype
    NEWS STORY
    >>> print docs[0].date_time
    03/14/1998 10:36:00
    >>> print docs[0].headline
    (DOCUMENT Kenyans protest tax hikes)
    >>> print docs[0].text # doctest: +ELLIPSIS
    (DOCUMENT
      (LOCATION NAIROBI)
      ,
      (LOCATION Kenya)
      (
      (ORGANIZATION AP)
      )
      _
      (CARDINAL Thousands)
      of
      laborers,
      ...
      on
      (DATE Saturday)
      ...)

Parsed Corpora
==============

The Treebank corpora provide a syntactic parse for each sentence.  The
NLTK data package includes a 10% sample of the Penn Treebank (in
``treebank``), as well as the Sinica Treebank (in ``sinica_treebank``).

Reading the Penn Treebank (Wall Street Journal sample):

    >>> from nltk.corpus import treebank
    >>> print treebank.fileids() # doctest: +ELLIPSIS
    ['wsj_0001.mrg', 'wsj_0002.mrg', 'wsj_0003.mrg', 'wsj_0004.mrg', ...]
    >>> print treebank.words('wsj_0003.mrg')
    ['A', 'form', 'of', 'asbestos', 'once', 'used', '*', ...]
    >>> print treebank.tagged_words('wsj_0003.mrg')
    [('A', 'DT'), ('form', 'NN'), ('of', 'IN'), ...]
    >>> print treebank.parsed_sents('wsj_0003.mrg')[0] # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
    (S
      (S-TPC-1
        (NP-SBJ
          (NP (NP (DT A) (NN form)) (PP (IN of) (NP (NN asbestos))))
          (RRC ...)...)...)
      ...
      (VP (VBD reported) (SBAR (-NONE- 0) (S (-NONE- *T*-1))))
      (. .))

If you have access to a full installation of the Penn Treebank, NLTK 
can be configured to load it as well. Download the ``ptb`` package, 
and in the directory ``nltk_data/corpora/ptb`` place the ``BROWN`` 
and ``WSJ`` directories of the Treebank installation (symlinks work 
as well). Then use the ``ptb`` module instead of ``treebank``:

   >>> from nltk.corpus import ptb
   >>> print ptb.fileids() # doctest: +ELLIPSIS
   ['BROWN/CF/CF01.MRG', 'BROWN/CF/CF02.MRG', 'BROWN/CF/CF03.MRG', 'BROWN/CF/CF04.MRG', ...]
   >>> print ptb.words('WSJ/00/WSJ_0003.MRG')
   ['A', 'form', 'of', 'asbestos', 'once', 'used', '*', ...]
   >>> print ptb.tagged_words('WSJ/00/WSJ_0003.MRG')
   [('A', 'DT'), ('form', 'NN'), ('of', 'IN'), ...]

...and so forth, like ``treebank`` but with extended fileids. Categories 
specified in ``allcats.txt`` can be used to filter by genre; they consist 
of ``news`` (for WSJ articles) and names of the Brown subcategories 
(``fiction``, ``humor``, ``romance``, etc.):

   >>> ptb.categories()
   ['adventure', 'belles_lettres', 'fiction', 'humor', 'lore', 'mystery', 'news', 'romance', 'science_fiction']
   >>> print ptb.fileids('news') # doctest: +ELLIPSIS
   ['WSJ/00/WSJ_0001.MRG', 'WSJ/00/WSJ_0002.MRG', 'WSJ/00/WSJ_0003.MRG', ...]
   >>> print ptb.words(categories=['humor','fiction'])
   ['Thirty-three', 'Scotty', 'did', 'not', 'go', 'back', ...]

As PropBank and NomBank depend on the (WSJ portion of the) Penn Treebank, 
the modules ``propbank_ptb`` and ``nombank_ptb`` are provided for access 
to a full PTB installation.

Reading the Sinica Treebank:

    >>> from nltk.corpus import sinica_treebank
    >>> print sinica_treebank.sents()
    [['\xe4\xb8\x80'], ['\xe5\x8f\x8b\xe6\x83\x85'], ...]
    >>> sinica_treebank.parsed_sents()[25] # doctest: +NORMALIZE_WHITESPACE
    Tree('S',
        [Tree('NP',
            [Tree('Nba', ['\xe5\x98\x89\xe7\x8f\x8d'])]),
         Tree('V\xe2\x80\xa7\xe5\x9c\xb0',
            [Tree('VA11', ['\xe4\xb8\x8d\xe5\x81\x9c']),
             Tree('DE', ['\xe7\x9a\x84'])]),
         Tree('VA4', ['\xe5\x93\xad\xe6\xb3\xa3'])])

Reading the CoNLL 2007 Dependency Treebanks:

    >>> from nltk.corpus import conll2007
    >>> conll2007.sents('esp.train')[0]
    ['El', 'aumento', 'del', '\xc3\xadndice', 'de', 'desempleo', ...]
    >>> conll2007.parsed_sents('esp.train')[0]
    <DependencyGraph with 38 nodes>
    >>> print conll2007.parsed_sents('esp.train')[0].tree()
    (fortaleció
      (aumento El (del (índice (de (desempleo estadounidense)))))
      hoy
      considerablemente
      (al
        (euro
          (cotizaba
            ,
            que
            (a (15.35 las GMT))
            se
            (en (mercado el (de divisas) (de Fráncfort)))
            (a 0,9452_dólares)
            (frente_a , (0,9349_dólares los (de (mañana esta)))))))
      .)

NLTK also provides a corpus reader for the York-Toronto-Helsinki
Parsed Corpus of Old English Prose (YCOE); but the corpus itself is
not included in the NLTK data package.  If you install it yourself,
you can use NLTK to access it:

    >>> from nltk.corpus import ycoe
    >>> for tree in ycoe.parsed_sents('cocuraC')[:4]:
    ...     print tree # doctest: +SKIP
    (CP-THT
      (C +D+atte)
      (IP-SUB ...)
      ...
      (. .))
    (IP-MAT
      (IP-MAT-0
        (PP (P On) (NP (ADJ o+dre) (N wisan)))...)
      ...
      (. .))
    (IP-MAT
      (NP-NOM-x-2 *exp*)
      (NP-DAT-1 (D^D +D+am) (ADJ^D unge+dyldegum))
      ...
      (. .))
    (IP-MAT
      (ADVP (ADV Sw+a))
      (NP-NOM-x (PRO^N hit))
      (ADVP-TMP (ADV^T oft))
      ...
      (. .))

If the YCOE corpus is not available, you will get an error message
when you try to access it:

    >>> from nltk.corpus import ycoe
    >>> print ycoe # doctest: +SKIP
    Traceback (most recent call last):
    LookupError:
    **********************************************************************
      Resource 'corpora/ycoe' not found.  For installation
      instructions, please see <http://nltk.org/index.php/Installation>.
      Searched in:
        - ...
    **********************************************************************

Word Lists and Lexicons
=======================

The NLTK data package also includes a number of lexicons and word
lists.  These are accessed just like text corpora.  The following
examples illustrate the use of the wordlist corpora:

    >>> from nltk.corpus import names, stopwords, words
    >>> words.fileids()
    ['en', 'en-basic']
    >>> words.words('en') # doctest: +ELLIPSIS
    ['A', 'a', 'aa', 'aal', 'aalii', 'aam', 'Aani', 'aardvark', 'aardwolf', ...]

    >>> stopwords.fileids() # doctest: +ELLIPSIS
    ['danish', 'dutch', 'english', 'finnish', 'french', 'german', 'hungarian', ...]
    >>> stopwords.words('portuguese') # doctest: +ELLIPSIS
    ['de', 'a', 'o', 'que', 'e', 'do', 'da', 'em', 'um', 'para', ...] 
    >>> names.fileids()
    ['female.txt', 'male.txt']
    >>> names.words('male.txt') # doctest: +ELLIPSIS
    ['Aamir', 'Aaron', 'Abbey', 'Abbie', 'Abbot', 'Abbott', ...]
    >>> names.words('female.txt') # doctest: +ELLIPSIS
    ['Abagael', 'Abagail', 'Abbe', 'Abbey', 'Abbi', 'Abbie', ...]

The CMU Pronunciation Dictionary corpus contains pronounciation
transcriptions for over 100,000 words.  It can be accessed as a list
of entries (where each entry consists of a word, an identifier, and a
transcription) or as a dictionary from words to lists of
transcriptions.  Transcriptions are encoded as tuples of phoneme
strings.

    >>> from nltk.corpus import cmudict
    >>> print cmudict.entries()[653:659] # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
    [('acetate', ['AE1', 'S', 'AH0', 'T', 'EY2', 'T']),
    ('acetic', ['AH0', 'S', 'EH1', 'T', 'IH0', 'K']),
    ('acetic', ['AH0', 'S', 'IY1', 'T', 'IH0', 'K']),
    ('aceto', ['AA0', 'S', 'EH1', 'T', 'OW0']),
    ('acetochlor', ['AA0', 'S', 'EH1', 'T', 'OW0', 'K', 'L', 'AO2', 'R']),
    ('acetone', ['AE1', 'S', 'AH0', 'T', 'OW2', 'N'])]
    >>> # Load the entire cmudict corpus into a Python dictionary:
    >>> transcr = cmudict.dict()
    >>> print [transcr[w][0] for w in 'Natural Language Tool Kit'.lower().split()] # doctest: +NORMALIZE_WHITESPACE
    [['N', 'AE1', 'CH', 'ER0', 'AH0', 'L'],
     ['L', 'AE1', 'NG', 'G', 'W', 'AH0', 'JH'],
     ['T', 'UW1', 'L'],
     ['K', 'IH1', 'T']]

Categorized Corpora
===================

Several corpora included with NLTK contain documents that have been categorized for
topic, genre, polarity, etc.  In addition to the standard corpus interface, these
corpora provide access to the list of categories and the mapping between the documents
and their categories (in both directions).  Access the categories using the ``categories()``
method, e.g.:

    >>> from nltk.corpus import brown, movie_reviews, reuters
    >>> brown.categories() # doctest: +NORMALIZE_WHITESPACE
    ['adventure', 'belles_lettres', 'editorial', 'fiction', 'government', 'hobbies', 'humor',
    'learned', 'lore', 'mystery', 'news', 'religion', 'reviews', 'romance', 'science_fiction']
    >>> movie_reviews.categories()
    ['neg', 'pos']
    >>> reuters.categories() # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS
    ['acq', 'alum', 'barley', 'bop', 'carcass', 'castor-oil', 'cocoa',
    'coconut', 'coconut-oil', 'coffee', 'copper', 'copra-cake', 'corn',
    'cotton', 'cotton-oil', 'cpi', 'cpu', 'crude', 'dfl', 'dlr', ...]

This method has an optional argument that specifies a document or a list
of documents, allowing us to map from (one or more) documents to (one or more) categories:

    >>> brown.categories('ca01')
    ['news']
    >>> brown.categories(['ca01','cb01'])
    ['editorial', 'news']
    >>> reuters.categories('training/9865')
    ['barley', 'corn', 'grain', 'wheat']
    >>> reuters.categories(['training/9865', 'training/9880'])
    ['barley', 'corn', 'grain', 'money-fx', 'wheat']

We can go back the other way using the optional argument of the ``fileids()`` method:

    >>> reuters.fileids('barley') # doctest: +ELLIPSIS
    ['test/15618', 'test/15649', 'test/15676', 'test/15728', 'test/15871', ...]

Both the ``categories()`` and ``fileids()`` methods return a sorted list containing
no duplicates.

In addition to mapping between categories and documents, these corpora permit
direct access to their contents via the categories.  Instead of accessing a subset
of a corpus by specifying one or more fileids, we can identify one or more categories, e.g.:

    >>> brown.tagged_words(categories='news')
    [('The', 'AT'), ('Fulton', 'NP-TL'), ...]
    >>> brown.sents(categories=['editorial','reviews']) # doctest: +NORMALIZE_WHITESPACE
    [['Assembly', 'session', 'brought', 'much', 'good'], ['The', 'General',
    'Assembly', ',', 'which', 'adjourns', 'today', ',', 'has', 'performed',
    'in', 'an', 'atmosphere', 'of', 'crisis', 'and', 'struggle', 'from',
    'the', 'day', 'it', 'convened', '.'], ...]

Note that it is an error to specify both documents and categories.

In the context of a text categorization system, we can easily test if the
category assigned to a document is correct as follows:

    >>> def classify(doc): return 'news'   # Trivial classifier
    >>> doc = 'ca01'
    >>> classify(doc) in brown.categories(doc)
    True

Propbank Corpus
===============
The Propbank corpus provides predicate-argument annotation for the
entire treebank.  Each verb in the treebank is annotated by a single
instance in the propbank corpus, containing information about the
location of the verb, and the location and identity of its arguments:

    >>> from nltk.corpus import propbank
    >>> pb_instances = propbank.instances()
    >>> print pb_instances # doctest: +NORMALIZE_WHITESPACE
    [<PropbankInstance: wsj_0001.mrg, sent 0, word 8>,
     <PropbankInstance: wsj_0001.mrg, sent 1, word 10>, ...]

Each propbank instance defines the following member variables:

  - Location information: `fileid`, `sentnum`, `wordnum`
  - Annotator information: `tagger`
  - Inflection information: `inflection`
  - Roleset identifier: `roleset`
  - Verb (aka predicate) location: `predicate`
  - Argument locations and types: `arguments`

The following examples show the types of these arguments:

    >>> inst = pb_instances[103]
    >>> (inst.fileid, inst.sentnum, inst.wordnum)
    ('wsj_0004.mrg', 8, 16)
    >>> inst.tagger
    'gold'
    >>> inst.inflection
    <PropbankInflection: vp--a>
    >>> infl = inst.inflection
    >>> infl.form, infl.tense, infl.aspect, infl.person, infl.voice
    ('v', 'p', '-', '-', 'a')
    >>> inst.roleset
    'rise.01'
    >>> inst.predicate
    PropbankTreePointer(16, 0)
    >>> inst.arguments # doctest: +NORMALIZE_WHITESPACE
    ((PropbankTreePointer(0, 2), 'ARG1'),
     (PropbankTreePointer(13, 1), 'ARGM-DIS'),
     (PropbankTreePointer(17, 1), 'ARG4-to'),
     (PropbankTreePointer(20, 1), 'ARG3-from'))

The location of the predicate and of the arguments are encoded using
`PropbankTreePointer` objects, as well as `PropbankChainTreePointer`
objects and `PropbankSplitTreePointer` objects.  A
`PropbankTreePointer` consists of a `wordnum` and a `height`:

    >>> print inst.predicate.wordnum, inst.predicate.height
    16 0

This identifies the tree constituent that is headed by the word that
is the `wordnum`\ 'th token in the sentence, and whose span is found
by going `height` nodes up in the tree.  This type of pointer is only
useful if we also have the corresponding tree structure, since it
includes empty elements such as traces in the word number count.  The
trees for 10% of the standard propbank corpus are contained in the
`treebank` corpus:

    >>> tree = inst.tree

    >>> from nltk.corpus import treebank
    >>> assert tree == treebank.parsed_sents(inst.fileid)[inst.sentnum]

    >>> inst.predicate.select(tree)
    Tree('VBD', ['rose'])
    >>> for (argloc, argid) in inst.arguments:
    ...     print '%-10s %s' % (argid, argloc.select(tree).pprint(500)[:50])
    ARG1       (NP-SBJ (NP (DT The) (NN yield)) (PP (IN on) (NP (
    ARGM-DIS   (PP (IN for) (NP (NN example)))
    ARG4-to    (PP-DIR (TO to) (NP (CD 8.04) (NN %)))
    ARG3-from  (PP-DIR (IN from) (NP (CD 7.90) (NN %)))

Propbank tree pointers can be converted to standard tree locations,
which are usually easier to work with, using the `treepos()` method:

    >>> treepos = inst.predicate.treepos(tree)
    >>> print treepos, tree[treepos]
    (4, 0) (VBD rose)

In some cases, argument locations will be encoded using
`PropbankChainTreePointer`\ s (for trace chains) or
`PropbankSplitTreePointer`\ s (for discontinuous constituents).  Both
of these objects contain a single member variable, `pieces`,
containing a list of the constituent pieces.  They also define the
method `select()`, which will return a tree containing all the
elements of the argument.  (A new head node is created, labeled
"*CHAIN*" or "*SPLIT*", since the argument is not a single constituent
in the original tree).  Sentence #6 contains an example of an argument
that is both discontinuous and contains a chain:

    >>> inst = pb_instances[6]
    >>> inst.roleset
    'expose.01'
    >>> argloc, argid = inst.arguments[2]
    >>> argloc
    <PropbankChainTreePointer: 22:1,24:0,25:1*27:0>
    >>> argloc.pieces
    [<PropbankSplitTreePointer: 22:1,24:0,25:1>, PropbankTreePointer(27, 0)]
    >>> argloc.pieces[0].pieces
    ... # doctest: +NORMALIZE_WHITESPACE
    [PropbankTreePointer(22, 1), PropbankTreePointer(24, 0),
     PropbankTreePointer(25, 1)]
    >>> print argloc.select(inst.tree)
    (*CHAIN*
      (*SPLIT* (NP (DT a) (NN group)) (IN of) (NP (NNS workers)))
      (-NONE- *))

The propbank corpus also provides access to the frameset files, which
define the argument labels used by the annotations, on a per-verb
basis.  Each frameset file contains one or more predicates, such as
'turn' or 'turn_on', each of which is divided into coarse-grained word
senses called rolesets.  For each roleset, the frameset file provides
descriptions of the argument roles, along with examples.

    >>> expose_01 = propbank.roleset('expose.01')
    >>> turn_01 = propbank.roleset('turn.01')
    >>> print turn_01 # doctest: +ELLIPSIS
    <Element 'roleset' at ...>
    >>> for role in turn_01.findall("roles/role"):
    ...     print role.attrib['n'], role.attrib['descr']
    0 turner
    1 thing turning
    m direction, location

    >>> from xml.etree import ElementTree
    >>> print ElementTree.tostring(turn_01.find('example')).strip()
    <example name="transitive agentive">
      <text>
      John turned the key in the lock.
      </text>
      <arg n="0">John</arg>
      <rel>turned</rel>
      <arg n="1">the key</arg>
      <arg f="LOC" n="m">in the lock</arg>
    </example>

Note that the standard corpus distribution only contains 10% of the
treebank, so the parse trees are not available for instances starting
at 9353:

    >>> inst = pb_instances[9352]
    >>> inst.fileid
    'wsj_0199.mrg'
    >>> print inst.tree # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS
    (S (NP-SBJ (NNP Trinity)) (VP (VBD said) (SBAR (-NONE- 0) ...))
    >>> print inst.predicate.select(inst.tree)
    (VB begin)

    >>> inst = pb_instances[9353]
    >>> inst.fileid
    'wsj_0200.mrg'
    >>> print inst.tree
    None
    >>> print inst.predicate.select(inst.tree)
    Traceback (most recent call last):
      . . .
    ValueError: Parse tree not avaialable

However, if you supply your own version of the treebank corpus (by
putting it before the nltk-provided version on `nltk.data.path`, or 
by creating a `ptb` directory as described above and using the 
`propbank_ptb` module), then you can access the trees for all 
instances.

A list of the verb lemmas contained in propbank is returned by the
`propbank.verbs()` method:

    >>> propbank.verbs()
    ['abandon', 'abate', 'abdicate', 'abet', 'abide', ...]

Other Corpora
=============

senseval
--------
The Senseval 2 corpus is a word sense disambiguation corpus.  Each
item in the corpus corresponds to a single ambiguous word.  For each
of these words, the corpus contains a list of instances, corresponding
to occurences of that word.  Each instance provides the word; a list
of word senses that apply to the word occurrence; and the word's
context.

    >>> from nltk.corpus import senseval
    >>> senseval.fileids()
    ['hard.pos', 'interest.pos', 'line.pos', 'serve.pos']
    >>> senseval.instances('hard.pos')
    ... # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
    [SensevalInstance(word='hard-a',
        position=20,
        context=[('``', '``'), ('he', 'PRP'), ...('hard', 'JJ'), ...],
        senses=('HARD1',)),
     SensevalInstance(word='hard-a',
        position=10,
        context=[('clever', 'NNP'), ...('hard', 'JJ'), ('time', 'NN'), ...],
        senses=('HARD1',)), ...]

The following code looks at instances of the word 'interest', and
displays their local context (2 words on each side) and word sense(s):

    >>> for inst in senseval.instances('interest.pos')[:10]:
    ...     p = inst.position
    ...     left = ' '.join(w for (w,t) in inst.context[p-2:p])
    ...     word = ' '.join(w for (w,t) in inst.context[p:p+1])
    ...     right = ' '.join(w for (w,t) in inst.context[p+1:p+3])
    ...     senses = ' '.join(inst.senses)
    ...     print '%20s |%10s | %-15s -> %s' % (left, word, right, senses)
             declines in |  interest | rates .         -> interest_6
      indicate declining |  interest | rates because   -> interest_6
           in short-term |  interest | rates .         -> interest_6
                     4 % |  interest | in this         -> interest_5
            company with | interests | in the          -> interest_5
                  , plus |  interest | .               -> interest_6
                 set the |  interest | rate on         -> interest_6
                  's own |  interest | , prompted      -> interest_4
           principal and |  interest | is the          -> interest_6
            increase its |  interest | to 70           -> interest_5

ppattach
--------
The Prepositional Phrase Attachment corpus is a corpus of
prepositional phrase attachment decisions.  Each instance in the
corpus is encoded as a ``PPAttachment`` object:

    >>> from nltk.corpus import ppattach
    >>> ppattach.attachments('training') # doctest: +NORMALIZE_WHITESPACE
    [PPAttachment(sent='0', verb='join', noun1='board',
                  prep='as', noun2='director', attachment='V'),
     PPAttachment(sent='1', verb='is', noun1='chairman',
                  prep='of', noun2='N.V.', attachment='N'),
     ...]
    >>> inst = ppattach.attachments('training')[0]
    >>> (inst.sent, inst.verb, inst.noun1, inst.prep, inst.noun2)
    ('0', 'join', 'board', 'as', 'director')
    >>> inst.attachment
    'V'

semcor
------
The Brown Corpus, annotated with WordNet senses.

    >>> from nltk.corpus import semcor
    >>> semcor.words('brown2/tagfiles/br-n12.xml')  # doctest: +ELLIPSIS
    ['When', 'several', 'minutes', 'had', 'passed', ...]
    >>> sent = semcor.xml('brown2/tagfiles/br-n12.xml').findall('context/p/s')[0]
    >>> for wordform in sent.getchildren():
    ...     print wordform.text,
    ...     for key in wordform.keys():
    ...         print key + '=' + wordform.get(key),
    ...     print
    ...
    When cmd=ignore pos=WRB
    several lemma=several cmd=done wnsn=1 pos=JJ lexsn=5:00:00:some(a):00
    minutes lemma=minute cmd=done wnsn=1 pos=NN lexsn=1:28:00::
    had cmd=done pos=VBD ot=notag
    passed lemma=pass cmd=done wnsn=4 pos=VB lexsn=2:38:03::
    and cmd=ignore pos=CC
    Curt pn=person cmd=done lexsn=1:03:00:: pos=NNP lemma=person rdf=person wnsn=1
    had cmd=done pos=VBD ot=notag
    n't lemma=n't cmd=done wnsn=0 pos=RB lexsn=4:02:00::
    emerged lemma=emerge cmd=done wnsn=1 pos=VB lexsn=2:30:00::
    from cmd=ignore pos=IN
    the cmd=ignore pos=DT
    livery_stable lemma=livery_stable cmd=done wnsn=1 pos=NN lexsn=1:06:00::
    ,
    Brenner pn=person cmd=done lexsn=1:03:00:: pos=NNP lemma=person rdf=person wnsn=1
    re-entered lemma=re-enter cmd=done wnsn=1 pos=VB lexsn=2:38:00::
    the cmd=ignore pos=DT
    hotel lemma=hotel cmd=done wnsn=1 pos=NN lexsn=1:06:00::
    and cmd=ignore pos=CC
    faced lemma=face cmd=done wnsn=4 pos=VB lexsn=2:42:02::
    Summers pn=person cmd=done lexsn=1:03:00:: pos=NNP lemma=person rdf=person wnsn=1
    across cmd=ignore pos=IN
    the cmd=ignore pos=DT
    counter lemma=counter cmd=done wnsn=1 pos=NN lexsn=1:06:00::
    .


shakespeare
-----------
The Shakespeare corpus contains a set of Shakespeare plays, formatted
as XML files.  These corpora are returned as ElementTree objects:

    >>> from nltk.corpus import shakespeare
    >>> from xml.etree import ElementTree
    >>> shakespeare.fileids() # doctest: +ELLIPSIS
    ['a_and_c.xml', 'dream.xml', 'hamlet.xml', 'j_caesar.xml', ...]
    >>> play = shakespeare.xml('dream.xml')
    >>> print play # doctest: +ELLIPSIS
    <Element 'PLAY' at ...>
    >>> print '%s: %s' % (play[0].tag, play[0].text)
    TITLE: A Midsummer Night's Dream
    >>> personae = [persona.text for persona in
    ...             play.findall('PERSONAE/PERSONA')]
    >>> print personae # doctest: +ELLIPSIS
    ['THESEUS, Duke of Athens.', 'EGEUS, father to Hermia.', ...]
    >>> # Find and print speakers not listed as personae
    >>> names = [persona.split(',')[0] for persona in personae]
    >>> speakers = set(speaker.text for speaker in
    ...                play.findall('*/*/*/SPEAKER'))
    >>> print sorted(speakers.difference(names)) # doctest: +NORMALIZE_WHITESPACE
    ['ALL', 'COBWEB', 'DEMETRIUS', 'Fairy', 'HERNIA', 'LYSANDER',
     'Lion', 'MOTH', 'MUSTARDSEED', 'Moonshine', 'PEASEBLOSSOM',
     'Prologue', 'Pyramus', 'Thisbe', 'Wall']

toolbox
-------
The Toolbox corpus distributed with NLTK contains a sample lexicon and
several sample texts from the Rotokas language.  The Toolbox corpus
reader returns Toolbox files as XML ElementTree objects.  The
following example loads the Rotokas dictionary, and figures out the
distribution of part-of-speech tags for reduplicated words.

.. doctest: +SKIP

    >>> from nltk.corpus import toolbox
    >>> from nltk.probability import FreqDist
    >>> from xml.etree import ElementTree
    >>> import re
    >>> rotokas = toolbox.xml('rotokas.dic')
    >>> redup_pos_freqdist = FreqDist()
    >>> # Note: we skip over the first record, which is actually
    >>> # the header.
    >>> for record in rotokas[1:]:
    ...     lexeme = record.find('lx').text
    ...     if re.match(r'(.*)\1$', lexeme):
    ...         redup_pos_freqdist.inc(record.find('ps').text)
    >>> for item in redup_pos_freqdist.keys():
    ...     print item, redup_pos_freqdist[item]
    V 41
    N 14
    ??? 4

This example displays some records from a Rotokas text:

.. doctest: +SKIP

    >>> river = toolbox.xml('rotokas/river.txt', key='ref')
    >>> for record in river.findall('record')[:3]:
    ...     for piece in record:
    ...         if len(piece.text) > 60:
    ...             print '%-6s %s...' % (piece.tag, piece.text[:57])
    ...         else:
    ...             print '%-6s %s' % (piece.tag, piece.text)
    ref    Paragraph 1
    t      ``Viapau oisio              ra   ovaupasi                ...
    m      viapau   oisio              ra   ovau   -pa       -si    ...
    g      NEG      this way/like this and  forget -PROG     -2/3.DL...
    p      NEG      ???                CONJ V.I    -SUFF.V.3 -SUFF.V...
    f      ``No ken lus tingting wanema samting papa i bin tok,'' Na...
    fe     ``Don't forget what Dad said,'' yelled Naomi.
    ref    2
    t      Osa     Ira  ora  Reviti viapau uvupasiva.
    m      osa     Ira  ora  Reviti viapau uvu        -pa       -si ...
    g      as/like name and  name   NEG    hear/smell -PROG     -2/3...
    p      CONJ    N.PN CONJ N.PN   NEG    V.T        -SUFF.V.3 -SUF...
    f      Tasol Ila na David no bin harim toktok.
    fe     But Ila and David took no notice.
    ref    3
    t      Ikaupaoro                     rokosiva                   ...
    m      ikau      -pa       -oro      roko    -si       -va      ...
    g      run/hurry -PROG     -SIM      go down -2/3.DL.M -RP      ...
    p      V.T       -SUFF.V.3 -SUFF.V.4 ADV     -SUFF.V.4 -SUFF.VT....
    f      Tupela i bin hariap i go long wara .
    fe     They raced to the river.

timit
-----
The NLTK data package includes a fragment of the TIMIT
Acoustic-Phonetic Continuous Speech Corpus.  This corpus is broken
down into small speech samples, each of which is available as a wave
file, a phonetic transcription, and a tokenized word list.

    >>> from nltk.corpus import timit
    >>> print timit.utteranceids() # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
    ['dr1-fvmh0/sa1', 'dr1-fvmh0/sa2', 'dr1-fvmh0/si1466',
    'dr1-fvmh0/si2096', 'dr1-fvmh0/si836', 'dr1-fvmh0/sx116',
    'dr1-fvmh0/sx206', 'dr1-fvmh0/sx26', 'dr1-fvmh0/sx296', ...]

    >>> item = timit.utteranceids()[5]
    >>> print timit.phones(item) # doctest: +NORMALIZE_WHITESPACE
    ['h#', 'k', 'l', 'ae', 's', 'pcl', 'p', 'dh', 'ax',
     's', 'kcl', 'k', 'r', 'ux', 'ix', 'nx', 'y', 'ax',
     'l', 'eh', 'f', 'tcl', 't', 'hh', 'ae', 'n', 'dcl',
     'd', 'h#']
    >>> print timit.words(item)
    ['clasp', 'the', 'screw', 'in', 'your', 'left', 'hand']
    >>> timit.play(item) # doctest: +SKIP

The corpus reader can combine the word segmentation information with
the phonemes to produce a single tree structure:

    >>> for tree in timit.phone_trees(item):
    ...     print tree
    (S
      h#
      (clasp k l ae s pcl p)
      (the dh ax)
      (screw s kcl k r ux)
      (in ix nx)
      (your y ax)
      (left l eh f tcl t)
      (hand hh ae n dcl d)
      h#)

The start time and stop time of each phoneme, word, and sentence are
also available:

    >>> print timit.phone_times(item) # doctest: +ELLIPSIS
    [('h#', 0, 2190), ('k', 2190, 3430), ('l', 3430, 4326), ...]
    >>> print timit.word_times(item) # doctest: +ELLIPSIS
    [('clasp', 2190, 8804), ('the', 8804, 9734), ...]
    >>> print timit.sent_times(item)
    [('Clasp the screw in your left hand.', 0, 32154)]

We can use these times to play selected pieces of a speech sample:

    >>> timit.play(item, 2190, 8804) # 'clasp'  # doctest: +SKIP

The corpus reader can also be queried for information about the
speaker and sentence identifier for a given speech sample:

    >>> print timit.spkrid(item)
    dr1-fvmh0
    >>> print timit.sentid(item)
    sx116
    >>> print timit.spkrinfo(timit.spkrid(item)) # doctest: +NORMALIZE_WHITESPACE
    SpeakerInfo(id='VMH0',
                sex='F',
                dr='1',
                use='TRN',
                recdate='03/11/86',
                birthdate='01/08/60',
                ht='5\'05"',
                race='WHT',
                edu='BS',
                comments='BEST NEW ENGLAND ACCENT SO FAR')

    >>> # List the speech samples from the same speaker:
    >>> timit.utteranceids(spkrid=timit.spkrid(item)) # doctest: +ELLIPSIS
    ['dr1-fvmh0/sa1', 'dr1-fvmh0/sa2', 'dr1-fvmh0/si1466', ...]

rte
---
The RTE (Recognizing Textual Entailment) corpus was derived from the
RTE1, RTE2 and RTE3 datasets (dev and test data), and consists of a
list of XML-formatted 'text'/'hypothesis' pairs.

    >>> from nltk.corpus import rte
    >>> print rte.fileids() # doctest: +ELLIPSIS
    ['rte1_dev.xml', 'rte1_test.xml', 'rte2_dev.xml', ..., 'rte3_test.xml']
    >>> rtepairs = rte.pairs(['rte2_test.xml', 'rte3_test.xml'])
    >>> print rtepairs  # doctest: +ELLIPSIS
    [<RTEPair: gid=2-8>, <RTEPair: gid=2-9>, <RTEPair: gid=2-15>, ...]

In the gold standard test sets, each pair is labeled according to
whether or not the text 'entails' the hypothesis; the
entailment value is mapped to an integer 1 (True) or 0 (False).

    >>> rtepairs[5]
    <RTEPair: gid=2-23>
    >>> rtepairs[5].text # doctest: +NORMALIZE_WHITESPACE
    'His wife Strida won a seat in parliament after forging an alliance
    with the main anti-Syrian coalition in the recent election.'
    >>> rtepairs[5].hyp
    'Strida elected to parliament.'
    >>> rtepairs[5].value
    1

The RTE corpus also supports an ``xml()`` method which produces ElementTrees.

    >>> xmltree = rte.xml('rte3_dev.xml')
    >>> xmltree # doctest: +SKIP
    <Element entailment-corpus at ...>
    >>> xmltree[7].findtext('t') # doctest: +NORMALIZE_WHITESPACE
    "Mrs. Bush's approval ratings have remained very high, above 80%,
    even as her husband's have recently dropped below 50%."

verbnet
-------
The VerbNet corpus is a lexicon that divides verbs into classes, based
on their syntax-semantics linking behavior.  The basic elements in the
lexicon are verb lemmas, such as 'abandon' and 'accept', and verb
classes, which have identifiers such as 'remove-10.1' and
'admire-31.2-1'.  These class identifiers consist of a representitive
verb selected from the class, followed by a numerical identifier.  The
list of verb lemmas, and the list of class identifiers, can be
retrieved with the following methods:

    >>> from nltk.corpus import verbnet
    >>> verbnet.lemmas()[20:25]
    ['accelerate', 'accept', 'acclaim', 'accompany', 'accrue']
    >>> verbnet.classids()[:5]
    ['accompany-51.7', 'admire-31.2', 'admire-31.2-1', 'admit-65', 'adopt-93']

The `classids()` method may also be used to retrieve the classes that
a given lemma belongs to:

    >>> verbnet.classids('accept')
    ['approve-77', 'characterize-29.2-1-1', 'obtain-13.5.2']

The primary object in the lexicon is a class record, which is stored
as an ElementTree xml object.  The class record for a given class
identifier is returned by the `vnclass()` method:

    >>> verbnet.vnclass('remove-10.1') # doctest: +ELLIPSIS
    <Element 'VNCLASS' at ...>

The `vnclass()` method also accepts "short" identifiers, such as '10.1':

    >>> verbnet.vnclass('10.1') # doctest: +ELLIPSIS
    <Element 'VNCLASS' at ...>

See the Verbnet documentation, or the Verbnet files, for information
about the structure of this xml.  As an example, we can retrieve a
list of thematic roles for a given Verbnet class:

    >>> vn_31_2 = verbnet.vnclass('admire-31.2')
    >>> for themrole in vn_31_2.findall('THEMROLES/THEMROLE'):
    ...     print themrole.attrib['type'],
    ...     for selrestr in themrole.findall('SELRESTRS/SELRESTR'):
    ...         print '[%(Value)s%(type)s]' % selrestr.attrib,
    ...     print
    Theme
    Experiencer [+animate]
    Predicate

The Verbnet corpus also provides a variety of pretty printing
functions that can be used to display the xml contents in a more
consise form.  The simplest such method is `pprint()`:

    >>> print verbnet.pprint('57')
    weather-57
      Subclasses: (none)
      Members: blow clear drizzle fog freeze gust hail howl lightning mist
        mizzle pelt pour precipitate rain roar shower sleet snow spit spot
        sprinkle storm swelter teem thaw thunder
      Thematic roles:
        * Theme[+concrete +force]
      Frames:
        Intransitive (Expletive Subject)
          Syntax: LEX[it] LEX[[+be]] VERB
          Semantics:
            * weather(during(E), Weather_type, ?Theme)
        NP (Expletive Subject, Theme Object)
          Syntax: LEX[it] LEX[[+be]] VERB NP[Theme]
          Semantics:
            * weather(during(E), Weather_type, Theme)
        PP (Expletive Subject, Theme-PP)
          Syntax: LEX[it[+be]] VERB PREP[with] NP[Theme]
          Semantics:
            * weather(during(E), Weather_type, Theme)

nps_chat
--------

The NPS Chat Corpus, Release 1.0 consists of over 10,000 posts in age-specific
chat rooms, which have been anonymized, POS-tagged and dialogue-act tagged.

    >>> print nltk.corpus.nps_chat.words()
    ['now', 'im', 'left', 'with', 'this', 'gay', 'name', ...]
    >>> print nltk.corpus.nps_chat.tagged_words()
    [('now', 'RB'), ('im', 'PRP'), ('left', 'VBD'), ...]
    >>> print nltk.corpus.nps_chat.tagged_posts() # doctest: +NORMALIZE_WHITESPACE
    [[('now', 'RB'), ('im', 'PRP'), ('left', 'VBD'), ('with', 'IN'),
    ('this', 'DT'), ('gay', 'JJ'), ('name', 'NN')], [(':P', 'UH')], ...]

We can access the XML elements corresponding to individual posts.  These elements
have ``class`` and ``user`` attributes that we can access using ``p.attrib['class']``
and ``p.attrib['user']``.  They also have text content, accessed using ``p.text``.

    >>> print nltk.corpus.nps_chat.xml_posts() # doctest: +ELLIPSIS
    [<Element 'Post' at 0x...>, <Element 'Post' at 0x...>, ...]
    >>> posts = nltk.corpus.nps_chat.xml_posts()
    >>> nltk.FreqDist(p.attrib['class'] for p in posts).keys() # doctest: +NORMALIZE_WHITESPACE
    ['Statement', 'System', 'Greet', 'Emotion', 'ynQuestion',
    'whQuestion', 'Accept', 'Bye', 'Emphasis', 'Continuer',
    'Reject', 'yAnswer', 'nAnswer', 'Clarify', 'Other']
    >>> posts[0].text
    'now im left with this gay name'

In addition to the above methods for accessing tagged text, we can navigate
the XML structure directly, as follows:

    >>> tokens = posts[0].findall('terminals/t')
    >>> [t.attrib['pos'] + "/" + t.attrib['word'] for t in tokens]
    ['RB/now', 'PRP/im', 'VBD/left', 'IN/with', 'DT/this', 'JJ/gay', 'NN/name']


---------------------
Corpus Reader Classes
---------------------

NLTK's *corpus reader* classes are used to access the contents of a
diverse set of corpora.  Each corpus reader class is specialized to
handle a specific corpus format.  Examples include the
`PlaintextCorpusReader`, which handles corpora that consist of a set
of unannotated text files, and the `BracketParseCorpusReader`, which
handles corpora that consist of files containing
parenthesis-delineated parse trees.

Automatically Created Corpus Reader Instances
=============================================

When then `nltk.corpus` module is imported, it automatically creates a
set of corpus reader instances that can be used to access the corpora
in the NLTK data distribution.  Here is a small sample of those
corpus reader instances:

    >>> import nltk
    >>> nltk.corpus.brown # doctest: +ELLIPSIS
    <CategorizedTaggedCorpusReader ...>
    >>> nltk.corpus.treebank # doctest: +ELLIPSIS
    <BracketParseCorpusReader ...>
    >>> nltk.corpus.names # doctest: +ELLIPSIS
    <WordListCorpusReader ...>
    >>> nltk.corpus.genesis # doctest: +ELLIPSIS
    <PlaintextCorpusReader ...>
    >>> nltk.corpus.inaugural # doctest: +ELLIPSIS
    <PlaintextCorpusReader ...>

This sample illustrates that different corpus reader classes are used
to read different corpora; but that the same corpus reader class may
be used for more than one corpus (e.g., ``genesis`` and ``inaugural``).

Creating New Corpus Reader Instances
====================================

Although the `nltk.corpus` module automatically creates corpus reader
instances for the corpora in the NLTK data distribution, you may
sometimes need to create your own corpus reader.  In particular, you
would need to create your own corpus reader if you want...

- To access a corpus that is not included in the NLTK data
  distribution.

- To access a full copy of a corpus for which the NLTK data
  distribution only provides a sample.

- To access a corpus using a customized corpus reader (e.g., with
  a customized tokenizer).

To create a new corpus reader, you will first need to look up the
signature for that corpus reader's constructor.  Different corpus
readers have different constructor signatures, but most of the
constructor signatures have the basic form::

    SomeCorpusReader(root, files, ...options...)

Where ``root`` is an absolute path to the directory containing the
corpus data files; ``files`` is either a list of file names (relative
to ``root``) or a regexp specifying which files should be included;
and ``options`` are additional reader-specific options.  For example,
we can create a customized corpus reader for the genesis corpus that
uses a different sentence tokenizer as follows:

    >>> # Find the directory where the corpus lives.
    >>> genesis_dir = nltk.data.find('corpora/genesis.zip').join('genesis/')
    >>> # Create our custom sentence tokenizer.
    >>> my_sent_tokenizer = nltk.RegexpTokenizer('[^.!?]+')
    >>> # Create the new corpus reader object.
    >>> my_genesis = nltk.corpus.PlaintextCorpusReader(
    ...     genesis_dir, '.*\.txt', sent_tokenizer=my_sent_tokenizer)
    >>> # Use the new corpus reader object.
    >>> print my_genesis.sents('english-kjv.txt')[0] # doctest: +NORMALIZE_WHITESPACE
    ['In', 'the', 'beginning', 'God', 'created', 'the', 'heaven',
     'and', 'the', 'earth']

If you wish to read your own plaintext corpus, which is stored in the
directory '/usr/share/some-corpus', then you can create a corpus
reader for it with::

    >>> my_corpus = nltk.corpus.PlaintextCorpusReader(
    ...     '/usr/share/some-corpus', '.*\.txt') # doctest: +SKIP

For a complete list of corpus reader subclasses, see the API
documentation for `nltk.corpus.CorpusReader`.

Corpus Types
============

Corpora vary widely in the types of content they include.  This is
reflected in the fact that the base class `CorpusReader` only defines
a few general-purpose methods for listing and accessing the files that
make up a corpus.  It is up to the subclasses to define *data access
methods* that provide access to the information in the corpus.
However, corpus reader subclasses should be consistent in their
definitions of these data access methods wherever possible.

At a high level, corpora can be divided into three basic types:

- A *token corpus* contains information about specific occurences of
  language use (or linguistic tokens), such as dialogues or written
  texts.  Examples of token corpora are collections of written text
  and collections of speech.

- A *type corpus*, or *lexicon*, contains information about a coherent
  set of lexical items (or linguistic types).  Examples of lexicons
  are dictionaries and word lists.

- A *language description corpus* contains information about a set of
  non-lexical linguistic constructs, such as grammar rules.

However, many individual corpora blur the distinctions between these
types.  For example, corpora that are primarily lexicons may include
token data in the form of example sentences; and corpora that are
primarily token corpora may be accompanied by one or more word lists
or other lexical data sets.

Because corpora vary so widely in their information content, we have
decided that it would not be wise to use separate corpus reader base
classes for different corpus types.  Instead, we simply try to make
the corpus readers consistent wherever possible, but let them differ
where the underlying data itself differs.

Common Corpus Reader Methods
============================

As mentioned above, there are only a handful of methods that all
corpus readers are guaranteed to implement.  These methods provide
access to the files that contain the corpus data.  Every corpus is
assumed to consist of one or more files, all located in a common root
directory (or in subdirectories of that root directory).  The absolute
path to the root directory is stored in the ``root`` property:

    >>> print nltk.corpus.genesis.root # doctest: +ELLIPSIS
    ...nltk_data/corpora/genesis...

Each file within the corpus is identified by a platform-independent
identifier, which is basically a path string that uses ``/`` as the
path seperator.  I.e., this identifier can be converted to a relative
path as follows:

    >>> some_corpus_file_id = nltk.corpus.reuters.fileids()[0]
    >>> import os.path
    >>> os.path.join(*some_corpus_file_id.split('/'))
    'test/14826'

To get a list of all data files that make up a corpus, use the
``fileids()`` method.  In some corpora, these files will not all contain
the same type of data; for example, for the ``nltk.corpus.timit``
corpus, ``fileids()`` will return a list including text files, word
segmentation files, phonetic transcription files, sound files, and
metadata files.  For corpora with diverse file types, the ``fileids()``
method will often take one or more optional arguments, which can be
used to get a list of the files with a specific file type:

    >>> nltk.corpus.timit.fileids() # doctest: +ELLIPSIS
    ['dr1-fvmh0/sa1.phn', 'dr1-fvmh0/sa1.txt', 'dr1-fvmh0/sa1.wav', ...]
    >>> nltk.corpus.timit.fileids('phn') # doctest: +ELLIPSIS
    ['dr1-fvmh0/sa1.phn', 'dr1-fvmh0/sa2.phn', 'dr1-fvmh0/si1466.phn', ...]

In some corpora, the files are divided into distinct categories.  For
these corpora, the ``fileids()`` method takes an optional argument,
which can be used to get a list of the files within a specific category:

    >>> nltk.corpus.brown.fileids('hobbies') # doctest: +ELLIPSIS
    ['ce01', 'ce02', 'ce03', 'ce04', 'ce05', 'ce06', 'ce07', ...]

The ``abspath()`` method can be used to find the absolute path to a
corpus file, given its file identifier:

    >>> print nltk.corpus.brown.abspath('ce06') # doctest: +ELLIPSIS
    .../corpora/brown/ce06

The ``abspaths()`` method can be used to find the absolute paths for
one corpus file, a list of corpus files, or (if no fileids are specified),
all corpus files.

This method is mainly useful as a helper method when defining corpus
data access methods, since data access methods can usually be called
with a string argument (to get a view for a specific file), with a
list argument (to get a view for a specific list of files), or with no
argument (to get a view for the whole corpus).

Data Access Methods
===================

Individual corpus reader subclasses typically extend this basic set of
file-access methods with one or more *data access methods*, which provide
easy access to the data contained in the corpus.  The signatures for
data access methods often have the basic form::

    corpus_reader.some_data access(fileids=None, ...options...)

Where ``fileids`` can be a single file identifier string (to get a view
for a specific file); a list of file identifier strings (to get a view
for a specific list of files); or None (to get a view for the entire
corpus).  Some of the common data access methods, and their return
types, are:

  - I{corpus}.words(): list of str
  - I{corpus}.sents(): list of (list of str)
  - I{corpus}.paras(): list of (list of (list of str))
  - I{corpus}.tagged_words(): list of (str,str) tuple
  - I{corpus}.tagged_sents(): list of (list of (str,str))
  - I{corpus}.tagged_paras(): list of (list of (list of (str,str)))
  - I{corpus}.chunked_sents(): list of (Tree w/ (str,str) leaves)
  - I{corpus}.parsed_sents(): list of (Tree with str leaves)
  - I{corpus}.parsed_paras(): list of (list of (Tree with str leaves))
  - I{corpus}.xml(): A single xml ElementTree
  - I{corpus}.raw(): str (unprocessed corpus contents)

For example, the `words()` method is supported by many different
corpora, and returns a flat list of word strings:

    >>> nltk.corpus.brown.words()
    ['The', 'Fulton', 'County', 'Grand', 'Jury', 'said', ...]
    >>> nltk.corpus.treebank.words()
    ['Pierre', 'Vinken', ',', '61', 'years', 'old', ',', ...]
    >>> nltk.corpus.conll2002.words()
    [u'Sao', u'Paulo', u'(', u'Brasil', u')', u',', u'23', ...]
    >>> nltk.corpus.genesis.words()
    [u'In', u'the', u'beginning', u'God', u'created', ...]

On the other hand, the `tagged_words()` method is only supported by
corpora that include part-of-speech annotations:

    >>> nltk.corpus.brown.tagged_words()
    [('The', 'AT'), ('Fulton', 'NP-TL'), ...]
    >>> nltk.corpus.treebank.tagged_words()
    [('Pierre', 'NNP'), ('Vinken', 'NNP'), (',', ','), ...]
    >>> nltk.corpus.conll2002.tagged_words()
    [(u'Sao', u'NC'), (u'Paulo', u'VMI'), (u'(', u'Fpa'), ...]
    >>> nltk.corpus.genesis.tagged_words()
    Traceback (most recent call last):
      ...
    AttributeError: 'PlaintextCorpusReader' object has no attribute 'tagged_words'

Although most corpus readers use file identifiers to index their
content, some corpora use different identifiers instead.  For example,
the data access methods for the ``timit`` corpus uses *utterance
identifiers* to select which corpus items should be returned:

    >>> nltk.corpus.timit.utteranceids() # doctest: +ELLIPSIS
    ['dr1-fvmh0/sa1', 'dr1-fvmh0/sa2', 'dr1-fvmh0/si1466', ...]
    >>> nltk.corpus.timit.words('dr1-fvmh0/sa2')
    ["don't", 'ask', 'me', 'to', 'carry', 'an', 'oily', 'rag', 'like', 'that']

Attempting to call ``timit``\ 's data access methods with a file
identifier will result in an exception:

    >>> nltk.corpus.timit.fileids() # doctest: +ELLIPSIS
    ['dr1-fvmh0/sa1.phn', 'dr1-fvmh0/sa1.txt', 'dr1-fvmh0/sa1.wav', ...]
    >>> nltk.corpus.timit.words('dr1-fvmh0/sa1.txt') # doctest: +ELLIPSIS
    Traceback (most recent call last):
      ...
    IOError:... No such file or directory: '.../dr1-fvmh0/sa1.txt.wrd'

As another example, the ``propbank`` corpus defines the ``roleset()``
method, which expects a roleset identifier, not a file identifier:

    >>> roleset = nltk.corpus.propbank.roleset('eat.01')
    >>> from xml.etree import ElementTree as ET
    >>> print ET.tostring(roleset) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
    <roleset id="eat.01" name="consume" vncls="39.1">
      <roles>
        <role descr="consumer, eater" n="0">...</role>...
      </roles>...
    </roleset>...

Stream Backed Corpus Views
==========================
An important feature of NLTK's corpus readers is that many of them
access the underlying data files using "corpus views."  A *corpus
view* is an object that acts like a simple data structure (such as a
list), but does not store the data elements in memory; instead, data
elements are read from the underlying data files on an as-needed
basis.

By only loading items from the file on an as-needesd basis, corpus
views maintain both memory efficiency and responsiveness.  The memory
efficiency of corpus readers is important because some corpora contain
very large amounts of data, and storing the entire data set in memory
could overwhelm many machines.  The responsiveness is important when
experimenting with corpora in interactive sessions and in in-class
demonstrations.

The most common corpus view is the `StreamBackedCorpusView`, which
acts as a read-only list of tokens.  Two additional corpus view
classes, `ConcatenatedCorpusView` and `LazySubsequence`, make it
possible to create concatenations and take slices of
`StreamBackedCorpusView` objects without actually storing the
resulting list-like object's elements in memory.

In the future, we may add additional corpus views that act like other
basic data structures, such as dictionaries.

Writing New Corpus Readers
==========================

In order to add support for new corpus formats, it is necessary to
define new corpus reader classes.  For many corpus formats, writing
new corpus readers is relatively streight-forward.  In this section,
we'll describe what's involved in creating a new corpus reader.  If
you do create a new corpus reader, we encourage you to contribute it
back to the NLTK project.

Don't Reinvent the Wheel
------------------------
Before you start writing a new corpus reader, you should check to be
sure that the desired format can't be read using an existing corpus
reader with appropriate constructor arguments.  For example, although
the `TaggedCorpusReader` assumes that words and tags are separated by
``/`` characters by default, an alternative tag-separation character
can be specified via the ``sep`` constructor argument.  You should
also check whether the new corpus format can be handled by subclassing
an existing corpus reader, and tweaking a few methods or variables.

Design
------
If you decide to write a new corpus reader from scratch, then you
should first decide which data access methods you want the reader to
provide, and what their signatures should be.  You should look at
existing corpus readers that process corpora with similar data
contents, and try to be consistent with those corpus readers whenever
possible.

You should also consider what sets of identifiers are appropriate for
the corpus format.  Where it's practical, file identifiers should be
used.  However, for some corpora, it may make sense to use additional
sets of identifiers.  Each set of identifiers should have a distinct
name (e.g., fileids, utteranceids, rolesets); and you should be consistent
in using that name to refer to that identifier.  Do not use parameter
names like ``id``, which leave it unclear what type of identifier is
required.

Once you've decided what data access methods and identifiers are
appropriate for your corpus, you should decide if there are any
customizable parameters that you'd like the corpus reader to handle.
These parameters make it possible to use a single corpus reader to
handle a wider variety of corpora.  The ``sep`` argument for
`TaggedCorpusReader`, mentioned above, is an example of a customizable
corpus reader parameter.

Implementation
--------------

Constructor
~~~~~~~~~~~
If your corpus reader implements any customizable parameters, then
you'll need to override the constructor.  Typically, the new
constructor will first call its base class's constructor, and then
store the customizable parameters.  For example, the
`ConllChunkCorpusReader`\ 's constructor is defined as follows:

    >>> def __init__(self, root, files, chunk_types):
    ...     CorpusReader.__init__(self, root, files)
    ...     self.chunk_types = tuple(chunk_types)

If your corpus reader does not implement any customization parameters,
then you can often just inherit the base class's constructor.

Data Access Methods
~~~~~~~~~~~~~~~~~~~
The most common type of data access method takes an argument
identifying which files to access, and returns a view covering those
files.  This argument may be a a single file identifier string (to get
a view for a specific file); a list of file identifier strings (to get
a view for a specific list of files); or None (to get a view for the
entire corpus).  The method's implementation converts this argument to
a list of path names using the `abspaths()` method, which handles all
three value types (string, list, and None):

    >>> nltk.corpus.brown.abspaths() # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
    [FileSystemPathPointer('.../corpora/brown/ca01'),
     FileSystemPathPointer('.../corpora/brown/ca02'), ...]
    >>> nltk.corpus.brown.abspaths('ce06') # doctest: +ELLIPSIS
    [FileSystemPathPointer('.../corpora/brown/ce06')]
    >>> nltk.corpus.brown.abspaths(['ce06', 'ce07']) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
    [FileSystemPathPointer('.../corpora/brown/ce06'),
     FileSystemPathPointer('.../corpora/brown/ce07')]

An example of this type of method is the `words()` method, defined by
the `PlaintextCorpusReader` as follows:

    >>> def words(self, fileids=None):
    ...     return concat([self.CorpusView(fileid, self._read_word_block)
    ...                    for fileid in self.abspaths(fileids)])

This method first uses `abspaths()` to convert ``fileids`` to a list of
absolute paths.  It then creates a corpus view for each file, using
the `PlaintextCorpusReader._read_word_block()` method to read elements
from the data file (see the discussion of corpus views below).
Finally, it combines these corpus views using the
`nltk.corpus.reader.util.concat()` function.

When writing a corpus reader for a corpus that is never expected to be
very large, it can sometimes be appropriate to read the files
directly, rather than using a corpus view.  For example, the
`WordListCorpusView` class defines its `words()` method as follows:

    >>> def words(self, fileids=None):
    ...     return concat([[w for w in open(fileid).read().split('\n') if w]
    ...                    for fileid in self.abspaths(fileids)])

(This is usually more appropriate for lexicons than for token corpora.)

If the type of data returned by a data access method is one for which
NLTK has a conventional representation (e.g., words, tagged words, and
parse trees), then you should use that representation.  Otherwise, you
may find it necessary to define your own representation.  For data
structures that are relatively corpus-specific, it's usually best to
define new classes for these elements.  For example, the ``propbank``
corpus defines the `PropbankInstance` class to store the semantic role
labeling instances described by the corpus; and the ``ppattach``
corpus defines the `PPAttachment` class to store the prepositional
attachment instances described by the corpus.

Corpus Views
~~~~~~~~~~~~
.. (Much of the content for this section is taken from the
   StreamBackedCorpusView docstring.)

The heart of a `StreamBackedCorpusView` is its *block reader*
function, which reads zero or more tokens from a stream, and returns
them as a list.  A very simple example of a block reader is:

    >>> def simple_block_reader(stream):
    ...     return stream.readline().split()

This simple block reader reads a single line at a time, and returns a
single token (consisting of a string) for each whitespace-separated
substring on the line.  A `StreamBackedCorpusView` built from this
block reader will act like a read-only list of all the
whitespace-seperated tokens in an underlying file.

When deciding how to define the block reader for a given corpus,
careful consideration should be given to the size of blocks handled by
the block reader.  Smaller block sizes will increase the memory
requirements of the corpus view's internal data structures (by 2
integers per block).  On the other hand, larger block sizes may
decrease performance for random access to the corpus.  (But note that
larger block sizes will *not* decrease performance for iteration.)

Internally, the `StreamBackedCorpusView` class maintains a partial
mapping from token index to file position, with one entry per block.
When a token with a given index *i* is requested, the corpus view
constructs it as follows:

1. First, it searches the toknum/filepos mapping for the token index
   closest to (but less than or equal to) *i*.

2. Then, starting at the file position corresponding to that index, it
   reads one block at a time using the block reader until it reaches
   the requested token.

The toknum/filepos mapping is created lazily: it is initially empty,
but every time a new block is read, the block's initial token is added
to the mapping.  (Thus, the toknum/filepos map has one entry per
block.)

You can create your own corpus view in one of two ways:

1. Call the `StreamBackedCorpusView` constructor, and provide your
   block reader function via the ``block_reader`` argument.

2. Subclass `StreamBackedCorpusView`, and override the
   `read_block()` method.

The first option is usually easier, but the second option can allow
you to write a single `read_block` method whose behavior can be
customized by different parameters to the subclass's constructor.  For
an example of this design pattern, see the `TaggedCorpusView` class,
which is used by `TaggedCorpusView`.

----------------
Regression Tests
----------------

The following helper functions are used to create and then delete
testing corpora that are stored in temporary directories.  These
testing corpora are used to make sure the readers work correctly.

    >>> import tempfile, os.path, textwrap
    >>> def make_testcorpus(ext='', **fileids):
    ...     root = tempfile.mkdtemp()
    ...     for fileid, contents in fileids.items():
    ...         fileid += ext
    ...         f = open(os.path.join(root, fileid), 'w')
    ...         f.write(textwrap.dedent(contents))
    ...         f.close()
    ...     return root
    >>> def del_testcorpus(root):
    ...     for fileid in os.listdir(root):
    ...         os.remove(os.path.join(root, fileid))
    ...     os.rmdir(root)

Plaintext Corpus Reader
=======================
The plaintext corpus reader is used to access corpora that consist of
unprocessed plaintext data.  It assumes that paragraph breaks are
indicated by blank lines.  Sentences and words can be tokenized using
the default tokenizers, or by custom tokenizers specificed as
parameters to the constructor.

    >>> root = make_testcorpus(ext='.txt',
    ...     a="""\
    ...     This is the first sentence.  Here is another
    ...     sentence!  And here's a third sentence.
    ...
    ...     This is the second paragraph.  Tokenization is currently
    ...     fairly simple, so the period in Mr. gets tokenized.
    ...     """,
    ...     b="""This is the second file.""")

    >>> from nltk.corpus.reader.plaintext import PlaintextCorpusReader

The list of documents can be specified explicitly, or implicitly (using a
regexp).  The ``ext`` argument specifies a file extension.

    >>> corpus = PlaintextCorpusReader(root, ['a.txt', 'b.txt'])
    >>> corpus.fileids()
    ['a.txt', 'b.txt']
    >>> corpus = PlaintextCorpusReader(root, '.*\.txt')
    >>> corpus.fileids()
    ['a.txt', 'b.txt']

The directory containing the corpus is corpus.root:

    >>> str(corpus.root) == str(root)
    True

We can get a list of words, or the raw string:

    >>> corpus.words()
    ['This', 'is', 'the', 'first', 'sentence', '.', 'Here', ...]
    >>> corpus.raw()[:40]
    'This is the first sentence.  Here is ano'

Check that reading individual documents works, and reading all documents at
once works:

    >>> len(corpus.words()), [len(corpus.words(d)) for d in corpus.fileids()]
    (46, [40, 6])
    >>> corpus.words('a.txt')
    ['This', 'is', 'the', 'first', 'sentence', '.', 'Here', ...]
    >>> corpus.words('b.txt')
    ['This', 'is', 'the', 'second', 'file', '.']
    >>> corpus.words()[:4], corpus.words()[-4:]
    (['This', 'is', 'the', 'first'], ['the', 'second', 'file', '.'])

We're done with the test corpus:

    >>> del_testcorpus(root)

Test the plaintext corpora that come with nltk:

    >>> from nltk.corpus import abc, genesis, inaugural
    >>> from nltk.corpus import state_union, webtext, udhr
    >>> for corpus in (abc, genesis, inaugural, state_union,
    ...                webtext, udhr):
    ...     print corpus
    ...     print '  ', `corpus.fileids()`[:60]
    ...     print '  ', `corpus.words()[:10]`[:60]
    ...     # doctest: +ELLIPSIS
    <PlaintextCorpusReader in '.../nltk_data/corpora/abc...>
       ['rural.txt', 'science.txt']
       ['PM', 'denies', 'knowledge', 'of', 'AWB', 'kickbacks', 'The
    <PlaintextCorpusReader in '.../nltk_data/corpora/genesis.zip/genesis/'>
       ['english-kjv.txt', 'english-web.txt', 'finnish.txt', 'frenc
       ['In', 'the', 'beginning', 'God', 'created', 'the', 'heaven'
    <PlaintextCorpusReader in '.../nltk_data/corpora/inaugural'>
       ['1789-Washington.txt', '1793-Washington.txt', '1797-Adams.t
       ['Fellow', '-', 'Citizens', 'of', 'the', 'Senate', 'and', 'o
    <PlaintextCorpusReader in '.../nltk_data/corpora/state_union.zip/state_union/'>
       ['1945-Truman.txt', '1946-Truman.txt', '1947-Truman.txt', '1
       ['PRESIDENT', 'HARRY', 'S', '.', 'TRUMAN', "'", 'S', 'ADDRES
    <PlaintextCorpusReader in '.../nltk_data/corpora/webtext.zip/webtext/'>
       ['firefox.txt', 'grail.txt', 'overheard.txt', 'pirates.txt',
       ['Cookie', 'Manager', ':', '"', 'Don', "'", 't', 'allow', 's
    <PlaintextCorpusReader in '.../nltk_data/corpora/udhr.zip/udhr/'>
       ['Abkhaz-Cyrillic+Abkh', 'Abkhaz-UTF8', 'Achehnese-Latin1',
       ['\xc0\xf3\xe0\xfe\xfb\xf2\xfa\xfb\xfe\xf1\xe0', '\xe8\xe7\x

Tagged Corpus Reader
====================
The Tagged Corpus reader can give us words, sentences, and paragraphs,
each tagged or untagged.  All of the read methods can take one item
(in which case they return the contents of that file) or a list of
documents (in which case they concatenate the contents of those files).
By default, they apply to all documents in the corpus.

    >>> root = make_testcorpus(
    ...     a="""\
    ...     This/det is/verb the/det first/adj sentence/noun ./punc
    ...     Here/det  is/verb  another/adj    sentence/noun ./punc
    ...     Note/verb that/comp you/pron can/verb use/verb \
    ...           any/noun tag/noun set/noun
    ...
    ...     This/det is/verb the/det second/adj paragraph/noun ./punc
    ...     word/n without/adj a/det tag/noun :/: hello ./punc
    ...     """,
    ...     b="""\
    ...     This/det is/verb the/det second/adj file/noun ./punc
    ...     """)

    >>> from nltk.corpus.reader.tagged import TaggedCorpusReader
    >>> corpus = TaggedCorpusReader(root, list('ab'))
    >>> corpus.fileids()
    ['a', 'b']
    >>> str(corpus.root) == str(root)
    True
    >>> corpus.words()
    ['This', 'is', 'the', 'first', 'sentence', '.', 'Here', ...]
    >>> corpus.sents() # doctest: +ELLIPSIS
    [['This', 'is', 'the', 'first', ...], ['Here', 'is', 'another'...], ...]
    >>> corpus.paras() # doctest: +ELLIPSIS
    [[['This', ...], ['Here', ...], ...], [['This', ...], ...], ...]
    >>> corpus.tagged_words() # doctest: +ELLIPSIS
    [('This', 'DET'), ('is', 'VERB'), ('the', 'DET'), ...]
    >>> corpus.tagged_sents() # doctest: +ELLIPSIS
    [[('This', 'DET'), ('is', 'VERB'), ...], [('Here', 'DET'), ...], ...]
    >>> corpus.tagged_paras() # doctest: +ELLIPSIS
    [[[('This', 'DET'), ...], ...], [[('This', 'DET'), ...], ...], ...]
    >>> corpus.raw()[:40]
    'This/det is/verb the/det first/adj sente'
    >>> len(corpus.words()), [len(corpus.words(d)) for d in corpus.fileids()]
    (38, [32, 6])
    >>> len(corpus.sents()), [len(corpus.sents(d)) for d in corpus.fileids()]
    (6, [5, 1])
    >>> len(corpus.paras()), [len(corpus.paras(d)) for d in corpus.fileids()]
    (3, [2, 1])
    >>> corpus.words('a')
    ['This', 'is', 'the', 'first', 'sentence', '.', 'Here', ...]
    >>> corpus.words('b')
    ['This', 'is', 'the', 'second', 'file', '.']
    >>> del_testcorpus(root)

The Brown Corpus uses the tagged corpus reader:

    >>> from nltk.corpus import brown
    >>> brown.fileids() # doctest: +ELLIPSIS
    ['ca01', 'ca02', 'ca03', 'ca04', 'ca05', 'ca06', 'ca07', ...]
    >>> brown.categories() # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
    ['adventure', 'belles_lettres', 'editorial', 'fiction', 'government', 'hobbies', 'humor',
    'learned', 'lore', 'mystery', 'news', 'religion', 'reviews', 'romance', 'science_fiction']
    >>> brown.root # doctest: +ELLIPSIS
    FileSystemPathPointer('.../corpora/brown')
    >>> brown.words()
    ['The', 'Fulton', 'County', 'Grand', 'Jury', 'said', ...]
    >>> brown.sents() # doctest: +ELLIPSIS
    [['The', 'Fulton', 'County', 'Grand', ...], ...]
    >>> brown.paras() # doctest: +ELLIPSIS
    [[['The', 'Fulton', 'County', ...]], [['The', 'jury', ...]], ...]
    >>> brown.tagged_words() # doctest: +ELLIPSIS
    [('The', 'AT'), ('Fulton', 'NP-TL'), ...]
    >>> brown.tagged_sents() # doctest: +ELLIPSIS
    [[('The', 'AT'), ('Fulton', 'NP-TL'), ('County', 'NN-TL'), ...], ...]
    >>> brown.tagged_paras() # doctest: +ELLIPSIS
    [[[('The', 'AT'), ...]], [[('The', 'AT'), ...]], ...]

Verbnet Corpus Reader
=====================

Make sure we're picking up the right number of elements:

    >>> from nltk.corpus import verbnet
    >>> len(verbnet.lemmas())
    3621
    >>> len(verbnet.wordnetids())
    4953
    >>> len(verbnet.classids())
    429

Selecting classids based on various selectors:

    >>> verbnet.classids(lemma='take') # doctest: +NORMALIZE_WHITESPACE
    ['bring-11.3', 'characterize-29.2', 'convert-26.6.2', 'cost-54.2',
    'fit-54.3', 'performance-26.7-2', 'steal-10.5']
    >>> verbnet.classids(wordnetid='lead%2:38:01')
    ['accompany-51.7']
    >>> verbnet.classids(fileid='approve-77.xml')
    ['approve-77']
    >>> verbnet.classids(classid='admire-31.2') # subclasses
    ['admire-31.2-1']

vnclass() accepts filenames, long ids, and short ids:

    >>> a = ElementTree.tostring(verbnet.vnclass('admire-31.2.xml'))
    >>> b = ElementTree.tostring(verbnet.vnclass('admire-31.2'))
    >>> c = ElementTree.tostring(verbnet.vnclass('31.2'))
    >>> a == b == c
    True

fileids() can be used to get files based on verbnet class ids:

    >>> verbnet.fileids('admire-31.2')
    ['admire-31.2.xml']
    >>> verbnet.fileids(['admire-31.2', 'obtain-13.5.2'])
    ['admire-31.2.xml', 'obtain-13.5.2.xml']
    >>> verbnet.fileids('badidentifier')
    Traceback (most recent call last):
      . . .
    ValueError: vnclass identifier 'badidentifier' not found

longid() and shortid() can be used to convert identifiers:

    >>> verbnet.longid('31.2')
    'admire-31.2'
    >>> verbnet.longid('admire-31.2')
    'admire-31.2'
    >>> verbnet.shortid('31.2')
    '31.2'
    >>> verbnet.shortid('admire-31.2')
    '31.2'
    >>> verbnet.longid('badidentifier')
    Traceback (most recent call last):
      . . .
    ValueError: vnclass identifier 'badidentifier' not found
    >>> verbnet.shortid('badidentifier')
    Traceback (most recent call last):
      . . .
    ValueError: vnclass identifier 'badidentifier' not found

Corpus View Regression Tests
============================

Select some corpus files to play with:

    >>> import nltk.data
    >>> # A very short file (160 chars):
    >>> f1 = nltk.data.find('corpora/inaugural/README')
    >>> # A relatively short file (791 chars):
    >>> f2 = nltk.data.find('corpora/inaugural/1793-Washington.txt')
    >>> # A longer file (32k chars):
    >>> f3 = nltk.data.find('corpora/inaugural/1909-Taft.txt')
    >>> fileids = [f1, f2, f3]

Check that corpus views produce the correct sequence of values.

    >>> from nltk.corpus.reader.util import *
    >>> linetok = nltk.LineTokenizer(blanklines='discard-eof')
    >>> for f in fileids:
    ...     v = StreamBackedCorpusView(f, read_whitespace_block)
    ...     assert list(v) == open(f).read().split()
    ...     v = StreamBackedCorpusView(f, read_line_block)
    ...     assert list(v) == linetok.tokenize(open(f).read())

Check that the corpus views report the correct lengths:

    >>> for f in fileids:
    ...     v = StreamBackedCorpusView(f, read_whitespace_block)
    ...     assert len(v) == len(open(f).read().split())
    ...     v = StreamBackedCorpusView(f, read_line_block)
    ...     assert len(v) == len(linetok.tokenize(open(f).read()))

Concatenation
-------------
Check that concatenation works as intended.

    >>> c1 = StreamBackedCorpusView(f1, read_whitespace_block)
    >>> c2 = StreamBackedCorpusView(f2, read_whitespace_block)
    >>> c3 = StreamBackedCorpusView(f3, read_whitespace_block)
    >>> c123 = c1+c2+c3
    >>> print c123
    ['C-Span', 'Inaugural', 'Address', 'Corpus', 'US', ...]

    >>> l1 = open(f1).read().split()
    >>> l2 = open(f2).read().split()
    >>> l3 = open(f3).read().split()
    >>> l123 = l1+l2+l3

    >>> list(c123) == l123
    True

    >>> (c1+c2+c3)[100] == l123[100]
    True

Slicing
-------
First, do some tests with fairly small slices.  These will all
generate tuple values.

    >>> from nltk.util import LazySubsequence
    >>> c1 = StreamBackedCorpusView(f1, read_whitespace_block)
    >>> l1 = open(f1).read().split()
    >>> print len(c1)
    21
    >>> len(c1) < LazySubsequence.MIN_SIZE
    True

Choose a list of indices, based on the length, that covers the
important corner cases:

    >>> indices = [-60, -30, -22, -21, -20, -1,
    ...            0, 1, 10, 20, 21, 22, 30, 60]

Test slicing with explicit start & stop value:

    >>> for s in indices:
    ...     for e in indices:
    ...         assert list(c1[s:e]) == l1[s:e]

Test slicing with stop=None:

    >>> for s in indices:
    ...     assert list(c1[s:]) == l1[s:]

Test slicing with start=None:

    >>> for e in indices:
    ...     assert list(c1[:e]) == l1[:e]

Test slicing with start=stop=None:

    >>> list(c1[:]) == list(l1[:])
    True

Next, we'll do some tests with much longer slices.  These will
generate LazySubsequence objects.

    >>> c3 = StreamBackedCorpusView(f3, read_whitespace_block)
    >>> l3 = open(f3).read().split()
    >>> print len(c3)
    5430
    >>> len(c3) > LazySubsequence.MIN_SIZE*2
    True

Choose a list of indices, based on the length, that covers the
important corner cases:

    >>> indices = [-12000, -6000, -5431, -5430, -5429, -3000, -200, -1,
    ...            0, 1, 200, 3000, 5000, 5429, 5430, 5431, 6000, 12000]

Test slicing with explicit start & stop value:

    >>> for s in indices:
    ...     for e in indices:
    ...         assert list(c3[s:e]) == l3[s:e]

Test slicing with stop=None:

    >>> for s in indices:
    ...     assert list(c3[s:]) == l3[s:]

Test slicing with start=None:

    >>> for e in indices:
    ...     assert list(c3[:e]) == l3[:e]

Test slicing with start=stop=None:

    >>> list(c3[:]) == list(l3[:])
    True

Multiple Iterators
------------------
If multiple iterators are created for the same corpus view, their
iteration can be interleaved:

    >>> c3 = StreamBackedCorpusView(f3, read_whitespace_block)
    >>> iterators = [c3.iterate_from(n) for n in [0,15,30,45]]
    >>> for i in range(15):
    ...     for iterator in iterators:
    ...         print '%-15s' % iterator.next(),
    ...     print
    My              a               duties          in             
    fellow          heavy           of              a              
    citizens:       weight          the             proper         
    Anyone          of              office          sense          
    who             responsibility. upon            of             
    has             If              which           the            
    taken           not,            he              obligation     
    the             he              is              which          
    oath            has             about           the            
    I               no              to              oath           
    have            conception      enter,          imposes.       
    just            of              or              The            
    taken           the             he              office         
    must            powers          is              of             
    feel            and             lacking         an             

SeekableUnicodeStreamReader
===========================

The file-like objects provided by the ``codecs`` module unfortunately
suffer from a bug that prevents them from working correctly with
corpus view objects.  In particular, although the expose ``seek()``
and ``tell()`` methods, those methods do not exhibit the expected
behavior, because they are not synchronized with the internal buffers
that are kept by the file-like objects.  For example, the ``tell()``
method will return the file position at the end of the buffers (whose
contents have not yet been returned by the stream); and therefore this
file position can not be used to return to the 'current' location in
the stream (since ``seek()`` has no way to reconstruct the buffers).

To get around these problems, we define a new class,
`SeekableUnicodeStreamReader`, to act as a file-like interface to
files containing encoded unicode data.  This class is loosely based on
the ``codecs.StreamReader`` class.  To construct a new reader, we call
the constructor with an underlying stream and an encoding name:

    >>> from cStringIO import StringIO
    >>> from nltk.data import SeekableUnicodeStreamReader
    >>> stream = StringIO(u"""\
    ... This is a test file.
    ... It is encoded in ascii.
    ... """.encode('ascii'))
    >>> reader = SeekableUnicodeStreamReader(stream, 'ascii')

`SeekableUnicodeStreamReader`\ s support all of the normal operations
supplied by a read-only stream.  Note that all of the read operations
return ``unicode`` objects (not ``str`` objects).

    >>> reader.read()         # read the entire file.
    u'This is a test file.\nIt is encoded in ascii.\n'
    >>> reader.seek(0)        # rewind to the start.
    >>> reader.read(5)        # read at most 5 bytes.
    u'This '
    >>> reader.readline()     # read to the end of the line.
    u'is a test file.\n'
    >>> reader.seek(0)        # rewind to the start.
    >>> for line in reader:
    ...     print `line`      # iterate over lines
    u'This is a test file.\n'
    u'It is encoded in ascii.\n'
    >>> reader.seek(0)        # rewind to the start.
    >>> reader.readlines()    # read a list of line strings
    [u'This is a test file.\n', u'It is encoded in ascii.\n']
    >>> reader.close()

Size argument to ``read()``
---------------------------
The ``size`` argument to ``read()`` specifies the maximum number of
*bytes* to read, not the maximum number of *characters*.  Thus, for
encodings that use multiple bytes per character, it may return fewer
characters than the ``size`` argument:

    >>> stream = StringIO(u"""\
    ... This is a test file.
    ... It is encoded in utf-16.
    ... """.encode('utf-16'))
    >>> reader = SeekableUnicodeStreamReader(stream, 'utf-16')
    >>> reader.read(10)
    u'This '

If a read block ends in the middle of the byte string encoding a
single character, then that byte string is stored in an internal
buffer, and re-used on the next call to ``read()``.  However, if the
size argument is too small to read even a single character, even
though at least one character is available, then the ``read()`` method
will read additional bytes until it can return a single character.
This ensures that the ``read()`` method does not return an empty
string, which could be mistaken for indicating the end of the file.

    >>> reader.seek(0)            # rewind to the start.
    >>> reader.read(1)            # we actually need to read 4 bytes
    u'T'
    >>> reader.tell()
    4

The ``readline()`` method may read more than a single line of text, in
which case it stores the text that it does not return in a buffer.  If
this buffer is not empty, then its contents will be included in the
value returned by the next call to ``read()``, regardless of the
``size`` argument, since they are available without reading any new
bytes from the stream:

    >>> reader.seek(0)            # rewind to the start.
    >>> reader.readline()         # stores extra text in a buffer
    u'This is a test file.\n'
    >>> print reader.linebuffer   # examine the buffer contents
    [u'It is encoded i']
    >>> reader.read(0)            # returns the contents of the buffer
    u'It is encoded i'
    >>> print reader.linebuffer   # examine the buffer contents
    None

Seek and Tell
-------------
In addition to these basic read operations,
`SeekableUnicodeStreamReader` also supports the ``seek()`` and
``tell()`` operations.  However, some care must still be taken when
using these operations.  In particular, the only file offsets that
should be passed to ``seek()`` are ``0`` and any offset that has been
returned by ``tell``.

    >>> stream = StringIO(u"""\
    ... This is a test file.
    ... It is encoded in utf-16.
    ... """.encode('utf-16'))
    >>> reader = SeekableUnicodeStreamReader(stream, 'utf-16')
    >>> reader.read(20)
    u'This is a '
    >>> pos = reader.tell(); print pos
    22
    >>> reader.read(20)
    u'test file.'
    >>> reader.seek(pos)     # rewind to the position from tell.
    >>> reader.read(20)
    u'test file.'

The ``seek()`` and ``tell()`` methods work property even when
``readline()`` is used.

    >>> stream = StringIO(u"""\
    ... This is a test file.
    ... It is encoded in utf-16.
    ... """.encode('utf-16'))
    >>> reader = SeekableUnicodeStreamReader(stream, 'utf-16')
    >>> reader.readline()
    u'This is a test file.\n'
    >>> pos = reader.tell(); print pos
    44
    >>> reader.readline()
    u'It is encoded in utf-16.\n'
    >>> reader.seek(pos)     # rewind to the position from tell.
    >>> reader.readline()
    u'It is encoded in utf-16.\n'

The following test performs a random series of reads, seeks, and
tells, and checks that the results are consistent:

    >>> import random
    >>> def reader_test(unicode_string, encoding, n=1000):
    ...     try: bytestr = unicode_string.encode(encoding)
    ...     except UnicodeEncodeError:
    ...         return 'codec can not encode string'
    ...     strlen = len(unicode_string)
    ...     stream = StringIO(bytestr)
    ...     reader = SeekableUnicodeStreamReader(stream, encoding)
    ...     # Find all character positions
    ...     chars = []
    ...     while True:
    ...         pos = reader.tell()
    ...         chars.append( (pos, reader.read(1)) )
    ...         if chars[-1][1] == '': break
    ...     # Find all strings
    ...     strings = dict( (pos,'') for (pos,c) in chars )
    ...     for pos1, char in chars:
    ...         for pos2, _ in chars:
    ...             if pos2 <= pos1:
    ...                 strings[pos2] += char
    ...     while True:
    ...         op = random.choice('tsrr')
    ...         # Check our position?
    ...         if op == 't': # tell
    ...             reader.tell()
    ...         # Perform a seek?
    ...         if op == 's': # seek
    ...             new_pos = random.choice([p for (p,c) in chars])
    ...             reader.seek(new_pos)
    ...         # Perform a read?
    ...         if op == 'r': # read
    ...             if random.random() < .3: pos = reader.tell()
    ...             else: pos = None
    ...             if random.random() < .2: size = None
    ...             elif random.random() < .8:
    ...                 size = random.randint(0, int(strlen/6))
    ...             else: size = random.randint(0, strlen+20)
    ...             if random.random() < .8:
    ...                 s = reader.read(size)
    ...             else:
    ...                 s = reader.readline(size)
    ...             # check that everything's consistent
    ...             if pos is not None:
    ...                 assert pos in strings
    ...                 assert strings[pos].startswith(s)
    ...                 n -= 1
    ...                 if n == 0:
    ...                     return 'passed'

Call the randomized test function `reader_test()` with a variety of
input strings and encodings.

    >>> encodings = ['ascii', 'latin1', 'greek', 'hebrew',
    ...              'utf-16', 'utf-7', 'utf-8']

    >>> strings = [
    ...     u"""\
    ... This is a test file.
    ... It is fairly short.""",
    ...     u"""\
    ... This file can be encoded with latin1. \x83""",
    ...     u"""\
    ... This is a test file.
    ... Here's a blank line:
    ...
    ... And here's some unicode: \xee \u0123 \uffe3
    ... """,
    ...     u"""\
    ... This is a test file.
    ... Unicode characters: \xf3 \u2222 \u3333\u4444 \u5555
    ... """,
    ...     u"""\
    ... This is a larger file.  It has some lines that are longer \
    ... than 72 characters.  It's got lots of repetition.  Here's \
    ... some unicode chars: \xee \u0123 \uffe3 \ueeee \u2345
    ...
    ... How fun!  Let's repeat it twenty times.
    ... """*20]

    >>> for i, string in enumerate(strings):
    ...     print 'String %d: %s' % (i, `string[:45]`[:-1]+'...')
    ...     for encoding in encodings:
    ...         print ('  - test for %-10s [%s]' %
    ...                (encoding, reader_test(string, encoding)))
    String 0: u'This is a test file.\nIt is fairly short....
      - test for ascii      [passed]
      - test for latin1     [passed]
      - test for greek      [passed]
      - test for hebrew     [passed]
      - test for utf-16     [passed]
      - test for utf-7      [passed]
      - test for utf-8      [passed]
    String 1: u'This file can be encoded with latin1. \x83...
      - test for ascii      [codec can not encode string]
      - test for latin1     [passed]
      - test for greek      [passed]
      - test for hebrew     [passed]
      - test for utf-16     [passed]
      - test for utf-7      [passed]
      - test for utf-8      [passed]
    String 2: u"This is a test file.\nHere's a blank line:\n\nAn...
      - test for ascii      [codec can not encode string]
      - test for latin1     [codec can not encode string]
      - test for greek      [codec can not encode string]
      - test for hebrew     [codec can not encode string]
      - test for utf-16     [passed]
      - test for utf-7      [passed]
      - test for utf-8      [passed]
    String 3: u'This is a test file.\nUnicode characters: \xf3 \u2222 ...
      - test for ascii      [codec can not encode string]
      - test for latin1     [codec can not encode string]
      - test for greek      [codec can not encode string]
      - test for hebrew     [codec can not encode string]
      - test for utf-16     [passed]
      - test for utf-7      [passed]
      - test for utf-8      [passed]
    String 4: u'This is a larger file.  It has some lines tha...
      - test for ascii      [codec can not encode string]
      - test for latin1     [codec can not encode string]
      - test for greek      [codec can not encode string]
      - test for hebrew     [codec can not encode string]
      - test for utf-16     [passed]
      - test for utf-7      [passed]
      - test for utf-8      [passed]

Squashed Bugs
=============

svn 5276 fixed a bug in the comment-stripping behavior of
parse_sexpr_block.

    >>> from cStringIO import StringIO
    >>> from nltk.corpus.reader.util import read_sexpr_block
    >>> f = StringIO("""
    ... (a b c)
    ... # This line is a comment.
    ... (d e f\ng h)""")
    >>> print read_sexpr_block(f, block_size=38, comment_char='#')
    ['(a b c)']
    >>> print read_sexpr_block(f, block_size=38, comment_char='#')
    ['(d e f\ng h)']

svn 5277 fixed a bug in parse_sexpr_block, which would cause it to
enter an infinite loop if a file ended mid-sexpr, or ended with a
token that was not followed by whitespace.  A related bug caused
an infinite loop if the corpus ended in an unmatched close paren --
this was fixed in svn 5279

    >>> f = StringIO("""
    ... This file ends mid-sexpr
    ... (hello (world""")
    >>> for i in range(3): print read_sexpr_block(f)
    ['This', 'file', 'ends', 'mid-sexpr']
    ['(hello (world']
    []

    >>> f = StringIO("""This file has no trailing whitespace.""")
    >>> for i in range(3): print read_sexpr_block(f)
    ['This', 'file', 'has', 'no', 'trailing']
    ['whitespace.']
    []

    >>> # Bug fixed in 5279:
    >>> f = StringIO("""a b c)""")
    >>> for i in range(3): print read_sexpr_block(f)
    ['a', 'b']
    ['c)']
    []


svn 5624 & 5265 fixed a bug in ConcatenatedCorpusView, which caused it
to return the wrong items when indexed starting at any index beyond
the first file.

    >>> import nltk
    >>> sents = nltk.corpus.brown.sents()
    >>> print sents[6000]
    ['Cholesterol', 'and', 'thyroid']
    >>> print sents[6000]
    ['Cholesterol', 'and', 'thyroid']

svn 5728 fixed a bug in Categorized*CorpusReader, which caused them
to return words from *all* files when just one file was specified.

    >>> from nltk.corpus import reuters
    >>> reuters.words('training/13080')
    ['U', '.', 'S', '.', 'SCIENTISTS', 'SAY', 'TROPICAL', ...]
    >>> reuters.words('training/5082')
    ['SHEPPARD', 'RESOURCES', 'TO', 'MERGE', 'WITH', ...]

svn 7227 fixed a bug in the qc corpus reader, which prevented
access to its tuples() method

    >>> from nltk.corpus import qc
    >>> qc.tuples('test.txt')
    [('NUM:dist', 'How far is it from Denver to Aspen ?'), ('LOC:city', 'What county is Modesto , California in ?'), ...]


