Sample usage for corpus

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:

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.

Corpus Reader Objects


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:
>>> print(str(nltk.corpus.brown).replace('\\\\','/'))
<CategorizedTaggedCorpusReader in '.../corpora/brown'...>
>>> # The Penn Treebank Corpus:
>>> print(str(nltk.corpus.treebank).replace('\\\\','/'))
<BracketParseCorpusReader in '.../corpora/treebank/combined'...>
>>> # The Name Genders Corpus:
>>> print(str(nltk.corpus.names).replace('\\\\','/'))
<WordListCorpusReader in '.../corpora/names'...>
>>> # The Inaugural Address Corpus:
>>> print(str(nltk.corpus.inaugural).replace('\\\\','/'))
<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()
['wsj_0001.mrg', 'wsj_0002.mrg', 'wsj_0003.mrg', 'wsj_0004.mrg', ...]
>>> nltk.corpus.inaugural.fileids()
['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')
'Fellow-Citizens of the Senate ...'
>>> inaugural.words('1789-Washington.txt')
['Fellow', '-', 'Citizens', 'of', 'the', ...]
>>> inaugural.sents('1789-Washington.txt')
[['Fellow', '-', 'Citizens'...], ['Among', 'the', 'vicissitudes'...]...]
>>> inaugural.paras('1789-Washington.txt')
[[['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())

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:

['PM', 'denies', 'knowledge', 'of', 'AWB', ...]
>>> nltk.corpus.genesis.words()
['In', 'the', 'beginning', 'God', 'created', ...]
>>> nltk.corpus.gutenberg.words(fileids='austen-emma.txt')
['[', 'Emma', 'by', 'Jane', 'Austen', '1816', ...]
>>> nltk.corpus.inaugural.words()
['Fellow', '-', 'Citizens', 'of', 'the', ...]
>>> nltk.corpus.state_union.words()
['PRESIDENT', 'HARRY', 'S', '.', 'TRUMAN', "'", ...]
>>> nltk.corpus.webtext.words()
['Cookie', 'Manager', ':', '"', 'Don', "'", 't', ...]

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', ...]
>>> print(brown.tagged_words())
[('The', 'AT'), ('Fulton', 'NP-TL'), ...]
>>> print(brown.sents())
[['The', 'Fulton', 'County'...], ['The', 'jury', 'further'...], ...]
>>> print(brown.tagged_sents())
[[('The', 'AT'), ('Fulton', 'NP-TL')...],
 [('The', 'AT'), ('jury', 'NN'), ('further', 'RBR')...]...]
>>> print(brown.paras(categories='reviews'))
[[['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'))
[[[('It', 'PPS'), ('is', 'BEZ'), ('not', '*'), ...],
  [('Certainly', 'RB'), ('not', '*'), ('in', 'IN'), ...]],
 [[('There', 'EX'), ('was', 'BEDZ'), ('about', 'IN'), ...],
  [('Not', '*'), ('the', 'AT'), ('noblest', 'JJT'), ...], ...], ...]

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

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

Several tagged corpora support access to a simplified, universal tagset, e.g. where all nouns tags are collapsed to a single category NOUN:

>>> print(brown.tagged_sents(tagset='universal'))
[[('The', 'DET'), ('Fulton', 'NOUN'), ('County', 'NOUN'), ('Grand', 'ADJ'), ('Jury', 'NOUN'), ...],
 [('The', 'DET'), ('jury', 'NOUN'), ('further', 'ADV'), ('said', 'VERB'), ('in', 'ADP'), ...]...]
>>> from nltk.corpus import conll2000, switchboard
>>> print(conll2000.tagged_words(tagset='universal'))
[('Confidence', 'NOUN'), ('in', 'ADP'), ...]

Use 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())
[['Confidence', 'in', 'the', 'pound', 'is', 'widely', ...],
 ['Chancellor', 'of', 'the', 'Exchequer', ...], ...]
>>> for tree in conll2000.chunked_sents()[:2]:
...     print(tree)
  (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)
  (PP of/IN)
  (NP the/DT Exchequer/NNP)
>>> print(conll2002.sents())
[['Sao', 'Paulo', '(', 'Brasil', ')', ',', ...], ['-'], ...]
>>> for tree in conll2002.chunked_sents()[:2]:
...     print(tree)
  (LOC Sao/NC Paulo/VMI)
  (LOC Brasil/NC)
(S -/Fg)


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


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', ...]
>>> semcor.chunks()
[['The'], ['Fulton', 'County', 'Grand', 'Jury'], ...]
>>> semcor.sents()
[['The', 'Fulton', 'County', 'Grand', 'Jury', 'said', ...],
['The', 'jury', 'further', 'said', ...], ...]
>>> semcor.chunk_sents()
[[['The'], ['Fulton', 'County', 'Grand', 'Jury'], ['said'], ...
['.']], [['The'], ['jury'], ['further'], ['said'], ... ['.']], ...]
>>> list(map(str, semcor.tagged_chunks(tag='both')[:3]))
['(DT The)', "(Lemma('') (NE (NNP Fulton County Grand Jury)))", "(Lemma('state.v.01.say') (VB said))"]
>>> [[str(c) for c in s] for s in semcor.tagged_sents(tag='both')[:2]]
[['(DT The)', "(Lemma('') (NE (NNP Fulton County Grand Jury)))", ...
 '(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()
['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)
>>> print(docs[0].doctype)
>>> print(docs[0].date_time)
03/14/1998 10:36:00
>>> print(docs[0].headline)
(DOCUMENT Kenyans protest tax hikes)
>>> print(docs[0].text)
  (LOCATION Kenya)
  (CARDINAL Thousands)
  (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())
['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])
      (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()) 
>>> 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')) 
['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] 
        [Tree('Nba', ['\xe5\x98\x89\xe7\x8f\x8d'])]),
        [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', 'índice', 'de', 'desempleo', ...]
>>> conll2007.parsed_sents('esp.train')[0] 
<DependencyGraph with 38 nodes>
>>> print(conll2007.parsed_sents('esp.train')[0].tree()) 
  (aumento El (del (índice (de (desempleo estadounidense)))))
        (a (15.35 las GMT))
        (en (mercado el (de divisas) (de Fráncfort)))
        (a 0,9452_dólares)
        (frente_a , (0,9349_dólares los (de (mañana esta)))))))

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')
['A', 'a', 'aa', 'aal', 'aalii', 'aam', 'Aani', 'aardvark', 'aardwolf', ...]
>>> stopwords.fileids() 
['arabic', 'azerbaijani', 'bengali', 'danish', 'dutch', 'english', 'finnish', 'french', ...]
>>> sorted(stopwords.words('portuguese'))
['a', 'ao', 'aos', 'aquela', 'aquelas', 'aquele', 'aqueles', ...]
>>> names.fileids()
['female.txt', 'male.txt']
>>> names.words('male.txt')
['Aamir', 'Aaron', 'Abbey', 'Abbie', 'Abbot', 'Abbott', ...]
>>> names.words('female.txt')
['Abagael', 'Abagail', 'Abbe', 'Abbey', 'Abbi', 'Abbie', ...]

The CMU Pronunciation Dictionary corpus contains pronunciation 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])
[('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()])
[['N', 'AE1', 'CH', 'ER0', 'AH0', 'L'],
 ['L', 'AE1', 'NG', 'G', 'W', 'AH0', 'JH'],
 ['T', 'UW1', 'L'],
 ['K', 'IH1', 'T']]


Please see the separate WordNet howto.


Please see the separate FrameNet howto.


Please see the separate PropBank howto.


Please see the separate SentiWordNet howto.

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()
['adventure', 'belles_lettres', 'editorial', 'fiction', 'government', 'hobbies', 'humor',
'learned', 'lore', 'mystery', 'news', 'religion', 'reviews', 'romance', 'science_fiction']
>>> movie_reviews.categories()
['neg', 'pos']
>>> reuters.categories()
['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')
>>> 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')
['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'])
[['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)

Other Corpora


A list of sentences from various sources, especially reviews and articles. Each line contains one sentence; sentences were separated by using a sentence tokenizer. Comparative sentences have been annotated with their type, entities, features and keywords.

>>> from nltk.corpus import comparative_sentences
>>> comparison = comparative_sentences.comparisons()[0]
>>> comparison.text
['its', 'fast-forward', 'and', 'rewind', 'work', 'much', 'more', 'smoothly',
'and', 'consistently', 'than', 'those', 'of', 'other', 'models', 'i', "'ve",
'had', '.']
>>> comparison.entity_2
>>> (comparison.feature, comparison.keyword)
('rewind', 'more')
>>> len(comparative_sentences.comparisons())

A list of positive and negative opinion words or sentiment words for English.

>>> from nltk.corpus import opinion_lexicon
>>> opinion_lexicon.words()[:4]
    ['2-faced', '2-faces', 'abnormal', 'abolish']

The OpinionLexiconCorpusReader also provides shortcuts to retrieve positive/negative words:

>>> opinion_lexicon.negative()[:4]
['2-faced', '2-faces', 'abnormal', 'abolish']

Note that words from words() method in opinion_lexicon are sorted by file id, not alphabetically:

>>> opinion_lexicon.words()[0:10]
['2-faced', '2-faces', 'abnormal', 'abolish', 'abominable', 'abominably',
'abominate', 'abomination', 'abort', 'aborted']
>>> sorted(opinion_lexicon.words())[0:10]
['2-faced', '2-faces', 'a+', 'abnormal', 'abolish', 'abominable', 'abominably',
'abominate', 'abomination', 'abort']

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')
[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
product_reviews_1 and product_reviews_2

These two datasets respectively contain annotated customer reviews of 5 and 9 products from

>>> from nltk.corpus import product_reviews_1
>>> camera_reviews ='Canon_G3.txt')
>>> review = camera_reviews[0]
>>> review.sents()[0]
['i', 'recently', 'purchased', 'the', 'canon', 'powershot', 'g3', 'and', 'am',
'extremely', 'satisfied', 'with', 'the', 'purchase', '.']
>>> review.features()
[('canon powershot g3', '+3'), ('use', '+2'), ('picture', '+2'),
('picture quality', '+1'), ('picture quality', '+1'), ('camera', '+2'),
('use', '+2'), ('feature', '+1'), ('picture quality', '+3'), ('use', '+1'),
('option', '+1')]

It is also possible to reach the same information directly from the stream:

>>> product_reviews_1.features('Canon_G3.txt')
[('canon powershot g3', '+3'), ('use', '+2'), ...]

We can compute stats for specific product features:

>>> n_reviews = len([(feat,score) for (feat,score) in product_reviews_1.features('Canon_G3.txt') if feat=='picture'])
>>> tot = sum([int(score) for (feat,score) in product_reviews_1.features('Canon_G3.txt') if feat=='picture'])
>>> mean = tot / n_reviews
>>> print(n_reviews, tot, mean)
15 24 1.6

A list of pros/cons sentences for determining context (aspect) dependent sentiment words, which are then applied to sentiment analysis of comparative sentences.

>>> from nltk.corpus import pros_cons
>>> pros_cons.sents(categories='Cons')
[['East', 'batteries', '!', 'On', '-', 'off', 'switch', 'too', 'easy',
'to', 'maneuver', '.'], ['Eats', '...', 'no', ',', 'GULPS', 'batteries'],
>>> pros_cons.words('IntegratedPros.txt')
['Easy', 'to', 'use', ',', 'economical', '!', ...]

The Brown Corpus, annotated with WordNet senses.

>>> from nltk.corpus import semcor
>>> semcor.words('brown2/tagfiles/br-n12.xml')
['When', 'several', 'minutes', 'had', 'passed', ...]

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 occurrences 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')
    context=[('``', '``'), ('he', 'PRP'), ...('hard', 'JJ'), ...],
    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

The Sentence Polarity dataset contains 5331 positive and 5331 negative processed sentences.

>>> from nltk.corpus import sentence_polarity
>>> sentence_polarity.sents()
[['simplistic', ',', 'silly', 'and', 'tedious', '.'], ["it's", 'so', 'laddish',
'and', 'juvenile', ',', 'only', 'teenage', 'boys', 'could', 'possibly', 'find',
'it', 'funny', '.'], ...]
>>> sentence_polarity.categories()
['neg', 'pos']
>>> sentence_polarity.sents()[1]
["it's", 'so', 'laddish', 'and', 'juvenile', ',', 'only', 'teenage', 'boys',
'could', 'possibly', 'find', 'it', 'funny', '.']

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()
['a_and_c.xml', 'dream.xml', 'hamlet.xml', 'j_caesar.xml', ...]
>>> play = shakespeare.xml('dream.xml')
>>> print(play)
<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)
['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)))
 'Lion', 'MOTH', 'MUSTARDSEED', 'Moonshine', 'PEASEBLOSSOM',
 'Prologue', 'Pyramus', 'Thisbe', 'Wall']

The Subjectivity Dataset contains 5000 subjective and 5000 objective processed sentences.

>>> from nltk.corpus import subjectivity
>>> subjectivity.categories()
['obj', 'subj']
>>> subjectivity.sents()[23]
['television', 'made', 'him', 'famous', ',', 'but', 'his', 'biggest', 'hits',
'happened', 'off', 'screen', '.']
>>> subjectivity.words(categories='subj')
['smart', 'and', 'alert', ',', 'thirteen', ...]

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.

This example displays some records from a Rotokas text:


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())
['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))
['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']

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)
  (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)

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

>>> print(timit.phone_times(item))
[('h#', 0, 2190), ('k', 2190, 3430), ('l', 3430, 4326), ...]
>>> print(timit.word_times(item))
[('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:

>>>, 2190, 8804) # 'clasp'  

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

>>> print(timit.spkrid(item))
>>> print(timit.sentid(item))
>>> print(timit.spkrinfo(timit.spkrid(item)))
            comments='BEST NEW ENGLAND ACCENT SO FAR')
>>> # List the speech samples from the same speaker:
>>> timit.utteranceids(spkrid=timit.spkrid(item))
['dr1-fvmh0/sa1', 'dr1-fvmh0/sa2', 'dr1-fvmh0/si1466', ...]

Twitter is well-known microblog service that allows public data to be collected via APIs. NLTK’s twitter corpus currently contains a sample of 20k Tweets retrieved from the Twitter Streaming API.

>>> from nltk.corpus import twitter_samples
>>> twitter_samples.fileids()
['negative_tweets.json', 'positive_tweets.json', 'tweets.20150430-223406.json']

We follow standard practice in storing full Tweets as line-separated JSON. These data structures can be accessed via However, in general it is more practical to focus just on the text field of the Tweets, which are accessed via the strings() method.

>>> twitter_samples.strings('tweets.20150430-223406.json')[:5]
['RT @KirkKus: Indirect cost of the UK being in the EU is estimated to be costing Britain \xa3170 billion per year! #BetterOffOut #UKIP', ...]

The default tokenizer for Tweets is specialised for ‘casual’ text, and the tokenized() method returns a list of lists of tokens.

>>> twitter_samples.tokenized('tweets.20150430-223406.json')[:5]
[['RT', '@KirkKus', ':', 'Indirect', 'cost', 'of', 'the', 'UK', 'being', 'in', ...],
 ['VIDEO', ':', 'Sturgeon', 'on', 'post-election', 'deals', ''], ...]

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())
['rte1_dev.xml', 'rte1_test.xml', 'rte2_dev.xml', ..., 'rte3_test.xml']
>>> rtepairs = rte.pairs(['rte2_test.xml', 'rte3_test.xml'])
>>> print(rtepairs)
[<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
'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

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

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

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 representative 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 classids() method may additionally be used to retrieve all classes within verbnet if nothing is passed:

>>> verbnet.classids()
['accompany-51.7', 'admire-31.2', 'admire-31.2-1', 'admit-65', 'adopt-93', 'advise-37.9', 'advise-37.9-1', 'allow-64', 'amalgamate-22.2', 'amalgamate-22.2-1', 'amalgamate-22.2-1-1', 'amalgamate-22.2-2', 'amalgamate-22.2-2-1', 'amalgamate-22.2-3', 'amalgamate-22.2-3-1', 'amalgamate-22.2-3-1-1', 'amalgamate-22.2-3-2', 'amuse-31.1', 'animal_sounds-38', 'appeal-31.4', 'appeal-31.4-1', 'appeal-31.4-2', 'appeal-31.4-3', 'appear-48.1.1', 'appoint-29.1', 'approve-77', 'assessment-34', 'assuming_position-50', 'avoid-52', 'banish-10.2', 'battle-36.4', 'battle-36.4-1', 'begin-55.1', 'begin-55.1-1', 'being_dressed-41.3.3', 'bend-45.2', 'berry-13.7', 'bill-54.5', 'body_internal_motion-49', 'body_internal_states-40.6', 'braid-41.2.2', 'break-45.1', 'breathe-40.1.2', 'breathe-40.1.2-1', 'bring-11.3', 'bring-11.3-1', 'build-26.1', 'build-26.1-1', 'bulge-47.5.3', 'bump-18.4', 'bump-18.4-1', 'butter-9.9', 'calibratable_cos-45.6', 'calibratable_cos-45.6-1', 'calve-28', 'captain-29.8', 'captain-29.8-1', 'captain-29.8-1-1', 'care-88', 'care-88-1', 'carry-11.4', 'carry-11.4-1', 'carry-11.4-1-1', 'carve-21.2', 'carve-21.2-1', 'carve-21.2-2', 'change_bodily_state-40.8.4', 'characterize-29.2', 'characterize-29.2-1', 'characterize-29.2-1-1', 'characterize-29.2-1-2', 'chase-51.6', 'cheat-10.6', 'cheat-10.6-1', 'cheat-10.6-1-1', 'chew-39.2', 'chew-39.2-1', 'chew-39.2-2', 'chit_chat-37.6', 'clear-10.3', 'clear-10.3-1', 'cling-22.5', 'coil-9.6', 'coil-9.6-1', 'coloring-24', 'complain-37.8', 'complete-55.2', 'concealment-16', 'concealment-16-1', 'confess-37.10', 'confine-92', 'confine-92-1', 'conjecture-29.5', 'conjecture-29.5-1', 'conjecture-29.5-2', 'consider-29.9', 'consider-29.9-1', 'consider-29.9-1-1', 'consider-29.9-1-1-1', 'consider-29.9-2', 'conspire-71', 'consume-66', 'consume-66-1', 'contiguous_location-47.8', 'contiguous_location-47.8-1', 'contiguous_location-47.8-2', 'continue-55.3', 'contribute-13.2', 'contribute-13.2-1', 'contribute-13.2-1-1', 'contribute-13.2-1-1-1', 'contribute-13.2-2', 'contribute-13.2-2-1', 'convert-26.6.2', 'convert-26.6.2-1', 'cooking-45.3', 'cooperate-73', 'cooperate-73-1', 'cooperate-73-2', 'cooperate-73-3', 'cope-83', 'cope-83-1', 'cope-83-1-1', 'correlate-86', 'correspond-36.1', 'correspond-36.1-1', 'correspond-36.1-1-1', 'cost-54.2', 'crane-40.3.2', 'create-26.4', 'create-26.4-1', 'curtsey-40.3.3', 'cut-21.1', 'cut-21.1-1', 'debone-10.8', 'declare-29.4', 'declare-29.4-1', 'declare-29.4-1-1', 'declare-29.4-1-1-1', 'declare-29.4-1-1-2', 'declare-29.4-1-1-3', 'declare-29.4-2', 'dedicate-79', 'defend-85', 'destroy-44', 'devour-39.4', 'devour-39.4-1', 'devour-39.4-2', 'differ-23.4', 'dine-39.5', 'disappearance-48.2', 'disassemble-23.3', 'discover-84', 'discover-84-1', 'discover-84-1-1', 'dress-41.1.1', 'dressing_well-41.3.2', 'drive-11.5', 'drive-11.5-1', 'dub-29.3', 'dub-29.3-1', 'eat-39.1', 'eat-39.1-1', 'eat-39.1-2', 'enforce-63', 'engender-27', 'entity_specific_cos-45.5', 'entity_specific_modes_being-47.2', 'equip-13.4.2', 'equip-13.4.2-1', 'equip-13.4.2-1-1', 'escape-51.1', 'escape-51.1-1', 'escape-51.1-2', 'escape-51.1-2-1', 'exceed-90', 'exchange-13.6', 'exchange-13.6-1', 'exchange-13.6-1-1', 'exhale-40.1.3', 'exhale-40.1.3-1', 'exhale-40.1.3-2', 'exist-47.1', 'exist-47.1-1', 'exist-47.1-1-1', 'feeding-39.7', 'ferret-35.6', 'fill-9.8', 'fill-9.8-1', 'fit-54.3', 'flinch-40.5', 'floss-41.2.1', 'focus-87', 'forbid-67', 'force-59', 'force-59-1', 'free-80', 'free-80-1', 'fulfilling-13.4.1', 'fulfilling-13.4.1-1', 'fulfilling-13.4.1-2', 'funnel-9.3', 'funnel-9.3-1', 'funnel-9.3-2', 'funnel-9.3-2-1', 'future_having-13.3', 'get-13.5.1', 'get-13.5.1-1', 'give-13.1', 'give-13.1-1', 'gobble-39.3', 'gobble-39.3-1', 'gobble-39.3-2', 'gorge-39.6', 'groom-41.1.2', 'grow-26.2', 'help-72', 'help-72-1', 'herd-47.5.2', 'hiccup-40.1.1', 'hit-18.1', 'hit-18.1-1', 'hold-15.1', 'hold-15.1-1', 'hunt-35.1', 'hurt-40.8.3', 'hurt-40.8.3-1', 'hurt-40.8.3-1-1', 'hurt-40.8.3-2', 'illustrate-25.3', 'image_impression-25.1', 'indicate-78', 'indicate-78-1', 'indicate-78-1-1', 'inquire-37.1.2', 'instr_communication-37.4', 'investigate-35.4', 'judgement-33', 'keep-15.2', 'knead-26.5', 'learn-14', 'learn-14-1', 'learn-14-2', 'learn-14-2-1', 'leave-51.2', 'leave-51.2-1', 'lecture-37.11', 'lecture-37.11-1', 'lecture-37.11-1-1', 'lecture-37.11-2', 'light_emission-43.1', 'limit-76', 'linger-53.1', 'linger-53.1-1', 'lodge-46', 'long-32.2', 'long-32.2-1', 'long-32.2-2', 'manner_speaking-37.3', 'marry-36.2', 'marvel-31.3', 'marvel-31.3-1', 'marvel-31.3-2', 'marvel-31.3-3', 'marvel-31.3-4', 'marvel-31.3-5', 'marvel-31.3-6', 'marvel-31.3-7', 'marvel-31.3-8', 'marvel-31.3-9', 'masquerade-29.6', 'masquerade-29.6-1', 'masquerade-29.6-2', 'matter-91', 'meander-47.7', 'meet-36.3', 'meet-36.3-1', 'meet-36.3-2', 'mine-10.9', 'mix-22.1', 'mix-22.1-1', 'mix-22.1-1-1', 'mix-22.1-2', 'mix-22.1-2-1', 'modes_of_being_with_motion-47.3', 'murder-42.1', 'murder-42.1-1', 'neglect-75', 'neglect-75-1', 'neglect-75-1-1', 'neglect-75-2', 'nonvehicle-51.4.2', 'nonverbal_expression-40.2', 'obtain-13.5.2', 'obtain-13.5.2-1', 'occurrence-48.3', 'order-60', 'order-60-1', 'orphan-29.7', 'other_cos-45.4', 'pain-40.8.1', 'pay-68', 'peer-30.3', 'pelt-17.2', 'performance-26.7', 'performance-26.7-1', 'performance-26.7-1-1', 'performance-26.7-2', 'performance-26.7-2-1', 'pit-10.7', 'pocket-9.10', 'pocket-9.10-1', 'poison-42.2', 'poke-19', 'pour-9.5', 'preparing-26.3', 'preparing-26.3-1', 'preparing-26.3-2', 'price-54.4', 'push-12', 'push-12-1', 'push-12-1-1', 'put-9.1', 'put-9.1-1', 'put-9.1-2', 'put_direction-9.4', 'put_spatial-9.2', 'put_spatial-9.2-1', 'reach-51.8', 'reflexive_appearance-48.1.2', 'refrain-69', 'register-54.1', 'rely-70', 'remove-10.1', 'risk-94', 'risk-94-1', 'roll-51.3.1', 'rummage-35.5', 'run-51.3.2', 'rush-53.2', 'say-37.7', 'say-37.7-1', 'say-37.7-1-1', 'say-37.7-2', 'scribble-25.2', 'search-35.2', 'see-30.1', 'see-30.1-1', 'see-30.1-1-1', 'send-11.1', 'send-11.1-1', 'separate-23.1', 'separate-23.1-1', 'separate-23.1-2', 'settle-89', 'shake-22.3', 'shake-22.3-1', 'shake-22.3-1-1', 'shake-22.3-2', 'shake-22.3-2-1', 'sight-30.2', 'simple_dressing-41.3.1', 'slide-11.2', 'slide-11.2-1-1', 'smell_emission-43.3', 'snooze-40.4', 'sound_emission-43.2', 'sound_existence-47.4', 'spank-18.3', 'spatial_configuration-47.6', 'split-23.2', 'spray-9.7', 'spray-9.7-1', 'spray-9.7-1-1', 'spray-9.7-2', 'stalk-35.3', 'steal-10.5', 'stimulus_subject-30.4', 'stop-55.4', 'stop-55.4-1', 'substance_emission-43.4', 'succeed-74', 'succeed-74-1', 'succeed-74-1-1', 'succeed-74-2', 'suffocate-40.7', 'suspect-81', 'swarm-47.5.1', 'swarm-47.5.1-1', 'swarm-47.5.1-2', 'swarm-47.5.1-2-1', 'swat-18.2', 'talk-37.5', 'tape-22.4', 'tape-22.4-1', 'tell-37.2', 'throw-17.1', 'throw-17.1-1', 'throw-17.1-1-1', 'tingle-40.8.2', 'touch-20', 'touch-20-1', 'transcribe-25.4', 'transfer_mesg-37.1.1', 'transfer_mesg-37.1.1-1', 'transfer_mesg-37.1.1-1-1', 'try-61', 'turn-26.6.1', 'turn-26.6.1-1', 'urge-58', 'vehicle-51.4.1', 'vehicle-51.4.1-1', 'waltz-51.5', 'want-32.1', 'want-32.1-1', 'want-32.1-1-1', 'weather-57', 'weekend-56', 'wink-40.3.1', 'wink-40.3.1-1', 'wipe_instr-10.4.2', 'wipe_instr-10.4.2-1', 'wipe_manner-10.4.1', 'wipe_manner-10.4.1-1', 'wish-62', 'withdraw-82', 'withdraw-82-1', 'withdraw-82-2', 'withdraw-82-3']

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')
<Element 'VNCLASS' at ...>

The vnclass() method also accepts “short” identifiers, such as ‘10.1’:

>>> verbnet.vnclass('10.1')
<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'], end=' ')
...     for selrestr in themrole.findall('SELRESTRS/SELRESTR'):
...         print('[%(Value)s%(type)s]' % selrestr.attrib, end=' ')
...     print()
Experiencer [+animate]

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

>>> print(verbnet.pprint('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]
    Intransitive (Expletive Subject)
      Example: It's raining.
      Syntax: LEX[it] LEX[[+be]] VERB
        * weather(during(E), Weather_type, ?Theme)
    NP (Expletive Subject, Theme Object)
      Example: It's raining cats and dogs.
      Syntax: LEX[it] LEX[[+be]] VERB NP[Theme]
        * weather(during(E), Weather_type, Theme)
    PP (Expletive Subject, Theme-PP)
      Example: It was pelting with rain.
      Syntax: LEX[it[+be]] VERB PREP[with] NP[Theme]
        * weather(during(E), Weather_type, Theme)

Verbnet gives us frames that link the syntax and semantics using an example. These frames are part of the corpus and we can use frames() to get a frame for a given verbnet class.

>>> frame = verbnet.frames('57')
>>> frame == [{'example': "It's raining.", 'description': {'primary': 'Intransitive', 'secondary': 'Expletive Subject'}, 'syntax': [{'pos_tag': 'LEX', 'modifiers': {'value': 'it', 'selrestrs': [], 'synrestrs': []}}, {'pos_tag': 'LEX', 'modifiers': {'value': '[+be]', 'selrestrs': [], 'synrestrs': []}}, {'pos_tag': 'VERB', 'modifiers': {'value': '', 'selrestrs': [], 'synrestrs': []}}], 'semantics': [{'predicate_value': 'weather', 'arguments': [{'type': 'Event', 'value': 'during(E)'}, {'type': 'VerbSpecific', 'value': 'Weather_type'}, {'type': 'ThemRole', 'value': '?Theme'}], 'negated': False}]}, {'example': "It's raining cats and dogs.", 'description': {'primary': 'NP', 'secondary': 'Expletive Subject, Theme Object'}, 'syntax': [{'pos_tag': 'LEX', 'modifiers': {'value': 'it', 'selrestrs': [], 'synrestrs': []}}, {'pos_tag': 'LEX', 'modifiers': {'value': '[+be]', 'selrestrs': [], 'synrestrs': []}}, {'pos_tag': 'VERB', 'modifiers': {'value': '', 'selrestrs': [], 'synrestrs': []}}, {'pos_tag': 'NP', 'modifiers': {'value': 'Theme', 'selrestrs': [], 'synrestrs': []}}], 'semantics': [{'predicate_value': 'weather', 'arguments': [{'type': 'Event', 'value': 'during(E)'}, {'type': 'VerbSpecific', 'value': 'Weather_type'}, {'type': 'ThemRole', 'value': 'Theme'}], 'negated': False}]}, {'example': 'It was pelting with rain.', 'description': {'primary': 'PP', 'secondary': 'Expletive Subject, Theme-PP'}, 'syntax': [{'pos_tag': 'LEX', 'modifiers': {'value': 'it[+be]', 'selrestrs': [], 'synrestrs': []}}, {'pos_tag': 'VERB', 'modifiers': {'value': '', 'selrestrs': [], 'synrestrs': []}}, {'pos_tag': 'PREP', 'modifiers': {'value': 'with', 'selrestrs': [], 'synrestrs': []}}, {'pos_tag': 'NP', 'modifiers': {'value': 'Theme', 'selrestrs': [], 'synrestrs': []}}], 'semantics': [{'predicate_value': 'weather', 'arguments': [{'type': 'Event', 'value': 'during(E)'}, {'type': 'VerbSpecific', 'value': 'Weather_type'}, {'type': 'ThemRole', 'value': 'Theme'}], 'negated': False}]}]

Verbnet corpus lets us access thematic roles individually using themroles().

>>> themroles = verbnet.themroles('57')
>>> themroles == [{'modifiers': [{'type': 'concrete', 'value': '+'}, {'type': 'force', 'value': '+'}], 'type': 'Theme'}]

Verbnet classes may also have subclasses sharing similar syntactic and semantic properties while having differences with the superclass. The Verbnet corpus allows us to access these subclasses using subclasses().

>>> print(verbnet.subclasses('9.1')) #Testing for 9.1 since '57' does not have subclasses
['put-9.1-1', 'put-9.1-2']

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', ...]
>>> print(nltk.corpus.nps_chat.tagged_words())
[('now', 'RB'), ('im', 'PRP'), ('left', 'VBD'), ...]
>>> print(nltk.corpus.nps_chat.tagged_posts())
[[('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())
[<Element 'Post' at 0...>, <Element 'Post' at 0...>, ...]
>>> posts = nltk.corpus.nps_chat.xml_posts()
>>> sorted(nltk.FreqDist(p.attrib['class'] for p in posts).keys())
['Accept', 'Bye', 'Clarify', 'Continuer', 'Emotion', 'Emphasis',
'Greet', 'Other', 'Reject', 'Statement', 'System', 'nAnswer',
'whQuestion', 'yAnswer', 'ynQuestion']
>>> 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']

The Multext-East Corpus consists of POS-tagged versions of George Orwell’s book 1984 in 12 languages: English, Czech, Hungarian, Macedonian, Slovenian, Serbian, Slovak, Romanian, Estonian, Farsi, Bulgarian and Polish. The corpus can be accessed using the usual methods for tagged corpora. The tagset can be transformed from the Multext-East specific MSD tags to the Universal tagset using the “tagset” parameter of all functions returning tagged parts of the corpus.

>>> print(nltk.corpus.multext_east.words("oana-en.xml"))
['It', 'was', 'a', 'bright', ...]
>>> print(nltk.corpus.multext_east.tagged_words("oana-en.xml"))
[('It', '#Pp3ns'), ('was', '#Vmis3s'), ('a', '#Di'), ...]
>>> print(nltk.corpus.multext_east.tagged_sents("oana-en.xml", "universal"))
[[('It', 'PRON'), ('was', 'VERB'), ('a', 'DET'), ...]

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 the 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
<CategorizedTaggedCorpusReader ...>
>>> nltk.corpus.treebank
<BracketParseCorpusReader ...>
>>> nltk.corpus.names
<WordListCorpusReader ...>
>>> nltk.corpus.genesis
<PlaintextCorpusReader ...>
>>> nltk.corpus.inaugural
<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 ='corpora/genesis')
>>> # Create our custom sentence tokenizer.
>>> my_sent_tokenizer = nltk.RegexpTokenizer('[^.!?]+')
>>> # Create the new corpus reader object.
>>> my_genesis = nltk.corpus.PlaintextCorpusReader(
...     genesis_dir, r'.*\.txt', sent_tokenizer=my_sent_tokenizer)
>>> # Use the new corpus reader object.
>>> print(my_genesis.sents('english-kjv.txt')[0])
['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', r'.*\.txt') 

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

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 occurrences 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:

>>> import os
>>> str(nltk.corpus.genesis.root).replace(os.path.sep,'/')

Each file within the corpus is identified by a platform-independent identifier, which is basically a path string that uses / as the path separator. 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.normpath(some_corpus_file_id).replace(os.path.sep,'/')

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()
['dr1-fvmh0/sa1.phn', 'dr1-fvmh0/sa1.txt', 'dr1-fvmh0/sa1.wav', ...]
>>> nltk.corpus.timit.fileids('phn')
['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')
['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:

>>> str(nltk.corpus.brown.abspath('ce06')).replace(os.path.sep,'/')

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', ...]
>>> nltk.corpus.treebank.words()
['Pierre', 'Vinken', ',', '61', 'years', 'old', ...]
>>> nltk.corpus.conll2002.words()
['Sao', 'Paulo', '(', 'Brasil', ')', ',', '23', ...]
>>> nltk.corpus.genesis.words()
['In', 'the', 'beginning', 'God', '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()
[('Sao', 'NC'), ('Paulo', 'VMI'), ('(', '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()
['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()
['dr1-fvmh0/sa1.phn', 'dr1-fvmh0/sa1.txt', 'dr1-fvmh0/sa1.wav', ...]
>>> nltk.corpus.timit.words('dr1-fvmh0/sa1.txt') 
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).decode('utf8'))
<roleset id="eat.01" name="consume" vncls="39.1">
    <role descr="consumer, eater" n="0">...</role>...

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-needed 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 straight-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.


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.


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, fileids, chunk_types, encoding='utf8',
...              tagset=None, separator=None):
...     ConllCorpusReader.__init__(
...             self, root, fileids, ('words', 'pos', 'chunk'),
...             chunk_types=chunk_types, encoding=encoding,
...             tagset=tagset, separator=separator)

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 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):

>>> print(str(nltk.corpus.brown.abspaths()).replace('\\\\','/'))
 FileSystemPathPointer('.../corpora/brown/ca02'), ...]
>>> print(str(nltk.corpus.brown.abspaths('ce06')).replace('\\\\','/'))
>>> print(str(nltk.corpus.brown.abspaths(['ce06', 'ce07'])).replace('\\\\','/'))

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

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-separated 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 specified 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, r'.*\.txt')
>>> corpus.fileids()
['a.txt', 'b.txt']

The directory containing the corpus is corpus.root:

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

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

>>> corpus.words()
['This', 'is', 'the', 'first', 'sentence', '.', ...]
>>> 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', '.', ...]
>>> 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
>>> for corpus in (abc, genesis, inaugural, state_union,
...                webtext):
...     print(str(corpus).replace('\\\\','/'))
...     print('  ', repr(corpus.fileids())[:60])
...     print('  ', repr(corpus.words()[:10])[:60])
<PlaintextCorpusReader in '.../nltk_data/corpora/ab...'>
   ['rural.txt', 'science.txt']
   ['PM', 'denies', 'knowledge', 'of', 'AWB', ...
<PlaintextCorpusReader in '.../nltk_data/corpora/genesi...'>
   ['english-kjv.txt', 'english-web.txt', 'finnish.txt', ...
   ['In', 'the', 'beginning', 'God', 'created', 'the', ...
<PlaintextCorpusReader in '.../nltk_data/corpora/inaugura...'>
   ['1789-Washington.txt', '1793-Washington.txt', ...
   ['Fellow', '-', 'Citizens', 'of', 'the', 'Senate', ...
<PlaintextCorpusReader in '.../nltk_data/corpora/state_unio...'>
   ['1945-Truman.txt', '1946-Truman.txt', ...
   ['PRESIDENT', 'HARRY', 'S', '.', 'TRUMAN', "'", ...
<PlaintextCorpusReader in '.../nltk_data/corpora/webtex...'>
   ['firefox.txt', 'grail.txt', 'overheard.txt', ...
   ['Cookie', 'Manager', ':', '"', 'Don', "'", 't', ...

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)
>>> corpus.words()
['This', 'is', 'the', 'first', 'sentence', '.', ...]
>>> corpus.sents()
[['This', 'is', 'the', 'first', ...], ['Here', 'is', 'another'...], ...]
>>> corpus.paras()
[[['This', ...], ['Here', ...], ...], [['This', ...], ...], ...]
>>> corpus.tagged_words()
[('This', 'DET'), ('is', 'VERB'), ('the', 'DET'), ...]
>>> corpus.tagged_sents()
[[('This', 'DET'), ('is', 'VERB'), ...], [('Here', 'DET'), ...], ...]
>>> corpus.tagged_paras()
[[[('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])
>>> print(corpus.words('a'))
['This', 'is', 'the', 'first', 'sentence', '.', ...]
>>> print(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()
['ca01', 'ca02', 'ca03', 'ca04', 'ca05', 'ca06', 'ca07', ...]
>>> brown.categories()
['adventure', 'belles_lettres', 'editorial', 'fiction', 'government', 'hobbies', 'humor',
'learned', 'lore', 'mystery', 'news', 'religion', 'reviews', 'romance', 'science_fiction']
>>> print(repr(brown.root).replace('\\\\','/'))
>>> brown.words()
['The', 'Fulton', 'County', 'Grand', 'Jury', ...]
>>> brown.sents()
[['The', 'Fulton', 'County', 'Grand', ...], ...]
>>> brown.paras()
[[['The', 'Fulton', 'County', ...]], [['The', 'jury', ...]], ...]
>>> brown.tagged_words()
[('The', 'AT'), ('Fulton', 'NP-TL'), ...]
>>> brown.tagged_sents()
[[('The', 'AT'), ('Fulton', 'NP-TL'), ('County', 'NN-TL'), ...], ...]
>>> brown.tagged_paras()
[[[('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())
>>> len(verbnet.wordnetids())
>>> len(verbnet.classids())

Selecting classids based on various selectors:

>>> verbnet.classids(lemma='take')
['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')
>>> verbnet.classids(fileid='approve-77.xml')
>>> verbnet.classids(classid='admire-31.2') # subclasses

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

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

>>> verbnet.fileids('admire-31.2')
>>> 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')
>>> verbnet.longid('admire-31.2')
>>> verbnet.shortid('31.2')
>>> verbnet.shortid('admire-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
>>> # A very short file (160 chars):
>>> f1 ='corpora/inaugural/README')
>>> # A relatively short file (791 chars):
>>> f2 ='corpora/inaugural/1793-Washington.txt')
>>> # A longer file (32k chars):
>>> f3 ='corpora/inaugural/1909-Taft.txt')
>>> fileids = [f1, f2, f3]

Check that concatenation works as intended.

>>> from nltk.corpus.reader.util import *
>>> c1 = StreamBackedCorpusView(f1, read_whitespace_block, encoding='utf-8')
>>> c2 = StreamBackedCorpusView(f2, read_whitespace_block, encoding='utf-8')
>>> c3 = StreamBackedCorpusView(f3, read_whitespace_block, encoding='utf-8')
>>> c123 = c1+c2+c3
>>> print(c123)
['C-Span', 'Inaugural', 'Address', 'Corpus', 'US', ...]
>>> l1 ='utf-8').read().split()
>>> l2 ='utf-8').read().split()
>>> l3 ='utf-8').read().split()
>>> l123 = l1+l2+l3
>>> list(c123) == l123
>>> (c1+c2+c3)[100] == l123[100]

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, encoding='utf-8')
>>> l1 ='utf-8').read().split()
>>> print(len(c1))
>>> len(c1) < LazySubsequence.MIN_SIZE

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[:])

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

>>> c3 = StreamBackedCorpusView(f3, read_whitespace_block, encoding='utf-8')
>>> l3 ='utf-8').read().split()
>>> print(len(c3))
>>> len(c3) > LazySubsequence.MIN_SIZE*2

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[:])
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' % next(iterator), end=' ')
...     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


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 io import StringIO, BytesIO
>>> from import SeekableUnicodeStreamReader
>>> stream = BytesIO(b"""\
... This is a test file.
... It is encoded in ascii.
... """.decode('ascii').encode('ascii'))
>>> reader = SeekableUnicodeStreamReader(stream, 'ascii')

SeekableUnicodeStreamReaders 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).

>>>         # read the entire file.
'This is a test file.\nIt is encoded in ascii.\n'
>>>        # rewind to the start.
>>>        # read at most 5 bytes.
'This '
>>> reader.readline()     # read to the end of the line.
'is a test file.\n'
>>>        # rewind to the start.
>>> for line in reader:
...     print(repr(line))      # iterate over lines
'This is a test file.\n'
'It is encoded in ascii.\n'
>>>        # rewind to the start.
>>> reader.readlines()    # read a list of line strings
['This is a test file.\n', '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 = BytesIO(b"""\
... This is a test file.
... It is encoded in utf-16.
... """.decode('ascii').encode('utf-16'))
>>> reader = SeekableUnicodeStreamReader(stream, 'utf-16')
'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.

>>>            # rewind to the start.
>>>            # we actually need to read 4 bytes
>>> int(reader.tell())

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:

>>>            # rewind to the start.
>>> reader.readline()         # stores extra text in a buffer
'This is a test file.\n'
>>> print(reader.linebuffer)   # examine the buffer contents
['It is encoded i']
>>>            # returns the contents of the buffer
'It is encoded i'
>>> print(reader.linebuffer)   # examine the buffer contents
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 = BytesIO(b"""\
... This is a test file.
... It is encoded in utf-16.
... """.decode('ascii').encode('utf-16'))
>>> reader = SeekableUnicodeStreamReader(stream, 'utf-16')
'This is a '
>>> pos = reader.tell(); print(pos)
'test file.'
>>>     # rewind to the position from tell.
'test file.'

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

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

Squashed Bugs

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

>>> from io import StringIO
>>> from nltk.corpus.reader.util import read_sexpr_block
>>> f = StringIO(b"""
... (a b c)
... # This line is a comment.
... (d e f\ng h)""".decode('ascii'))
>>> 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(b"""
... This file ends mid-sexpr
... (hello (world""".decode('ascii'))
>>> for i in range(3): print(read_sexpr_block(f))
['This', 'file', 'ends', 'mid-sexpr']
['(hello (world']
>>> f = StringIO(b"This file has no trailing whitespace.".decode('ascii'))
>>> for i in range(3): print(read_sexpr_block(f))
['This', 'file', 'has', 'no', 'trailing']
>>> # Bug fixed in 5279:
>>> f = StringIO(b"a b c)".decode('ascii'))
>>> for i in range(3): print(read_sexpr_block(f))
['a', 'b']

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/13085')
['SNYDER', '&', 'lt', ';', 'SOI', '>', 'MAKES', ...]
>>> reuters.words('training/5082')

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 ?'), ...]

Ensure that KEYWORD from no longer contains a ReDoS vulnerability.

>>> import re
>>> import time
>>> from nltk.corpus.reader.comparative_sents import KEYWORD
>>> sizes = {
...     "short": 4000,
...     "long": 40000
... }
>>> exec_times = {
...     "short": [],
...     "long": [],
... }
>>> for size_name, size in sizes.items():
...     for j in range(9):
...         start_t = time.perf_counter()
...         payload = "( " + "(" * size
...         output = KEYWORD.findall(payload)
...         exec_times[size_name].append(time.perf_counter() - start_t)
...     exec_times[size_name] = sorted(exec_times[size_name])[4] # Get the median

Ideally, the execution time of such a regular expression is linear in the length of the input. As such, we would expect exec_times[“long”] to be roughly 10 times as big as exec_times[“short”]. With the ReDoS in place, it took roughly 80 times as long. For now, we accept values below 30 (times as long), due to the potential for variance. This ensures that the ReDoS has certainly been reduced, if not removed.

>>> exec_times["long"] / exec_times["short"] < 30