.. Copyright (C) 2001-2018 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://www.nltk.org/nltk_data/ 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: >>> print(str(nltk.corpus.brown).replace('\\\\','/')) >>> # The Penn Treebank Corpus: >>> print(str(nltk.corpus.treebank).replace('\\\\','/')) >>> # The Name Genders Corpus: >>> print(str(nltk.corpus.names).replace('\\\\','/')) >>> # The Inaugural Address Corpus: >>> print(str(nltk.corpus.inaugural).replace('\\\\','/')) 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', ...] >>> 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', ...] >>> 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', ...] >>> 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()) # 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 Language POS-Tagged Corpus includes samples of Indian text annotated with part-of-speech tags: >>> from nltk.corpus import indian >>> print(indian.words()) # doctest: +SKIP ['\xe0\xa6\xae\xe0\xa6\xb9\xe0\xa6\xbf\...', '\xe0\xa6\xb8\xe0\xa6\xa8\xe0\xa7\x8d\xe0...', ...] >>> print(indian.tagged_words()) # doctest: +SKIP [('\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')) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE [[('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')) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE [('Confidence', 'NOUN'), ('in', 'ADP'), ...] 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', ...] >>> semcor.chunks() [['The'], ['Fulton', 'County', 'Grand', 'Jury'], ...] >>> 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)', "(Lemma('group.n.01.group') (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('group.n.01.group') (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() # doctest: +NORMALIZE_WHITESPACE ['APW_19980314', 'APW_19980424', 'APW_19980429', 'NYT_19980315', 'NYT_19980403', 'NYT_19980407'] >>> docs = ieer.parsed_docs('APW_19980314') >>> print(docs[0]) >>> 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: +SKIP ['BROWN/CF/CF01.MRG', 'BROWN/CF/CF02.MRG', 'BROWN/CF/CF03.MRG', 'BROWN/CF/CF04.MRG', ...] >>> print(ptb.words('WSJ/00/WSJ_0003.MRG')) # doctest: +SKIP ['A', 'form', 'of', 'asbestos', 'once', 'used', '*', ...] >>> print(ptb.tagged_words('WSJ/00/WSJ_0003.MRG')) # doctest: +SKIP [('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() # doctest: +SKIP ['adventure', 'belles_lettres', 'fiction', 'humor', 'lore', 'mystery', 'news', 'romance', 'science_fiction'] >>> print(ptb.fileids('news')) # doctest: +SKIP ['WSJ/00/WSJ_0001.MRG', 'WSJ/00/WSJ_0002.MRG', 'WSJ/00/WSJ_0003.MRG', ...] >>> print(ptb.words(categories=['humor','fiction'])) # doctest: +SKIP ['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()) # doctest: +SKIP [['\xe4\xb8\x80'], ['\xe5\x8f\x8b\xe6\x83\x85'], ...] >>> sinica_treebank.parsed_sents()[25] # doctest: +SKIP 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] # doctest: +SKIP ['El', 'aumento', 'del', 'índice', 'de', 'desempleo', ...] >>> conll2007.parsed_sents('esp.train')[0] # doctest: +SKIP >>> print(conll2007.parsed_sents('esp.train')[0].tree()) # doctest: +SKIP (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))))))) .) 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 ['arabic', 'azerbaijani', 'danish', 'dutch', 'english', 'finnish', 'french', ...] >>> 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']] WordNet ======= Please see the separate WordNet howto. FrameNet ======== Please see the separate FrameNet howto. PropBank ======== Please see the separate PropBank howto. SentiWordNet ============ 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() # 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 Other Corpora ============= comparative_sentences --------------------- 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 'models' >>> (comparison.feature, comparison.keyword) ('rewind', 'more') >>> len(comparative_sentences.comparisons()) 853 opinion_lexicon --------------- 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'] 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' product_reviews_1 and product_reviews_2 --------------------------------------- These two datasets respectively contain annotated customer reviews of 5 and 9 products from amazon.com. >>> from nltk.corpus import product_reviews_1 >>> camera_reviews = product_reviews_1.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']) >>> # We use float for backward compatibility with division in Python2.7 >>> mean = tot/float(n_reviews) >>> print(n_reviews, tot, mean) 15 24 1.6 pros_cons --------- 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', '!', ...] 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, end=' ') ... for key in sorted(wordform.keys()): ... print(key + '=' + wordform.get(key), end=' ') ... print() ... When cmd=ignore pos=WRB several cmd=done lemma=several lexsn=5:00:00:some(a):00 pos=JJ wnsn=1 minutes cmd=done lemma=minute lexsn=1:28:00:: pos=NN wnsn=1 had cmd=done ot=notag pos=VBD passed cmd=done lemma=pass lexsn=2:38:03:: pos=VB wnsn=4 and cmd=ignore pos=CC Curt cmd=done lemma=person lexsn=1:03:00:: pn=person pos=NNP rdf=person wnsn=1 had cmd=done ot=notag pos=VBD n't cmd=done lemma=n't lexsn=4:02:00:: pos=RB wnsn=0 emerged cmd=done lemma=emerge lexsn=2:30:00:: pos=VB wnsn=1 from cmd=ignore pos=IN the cmd=ignore pos=DT livery_stable cmd=done lemma=livery_stable lexsn=1:06:00:: pos=NN wnsn=1 , Brenner cmd=done lemma=person lexsn=1:03:00:: pn=person pos=NNP rdf=person wnsn=1 re-entered cmd=done lemma=re-enter lexsn=2:38:00:: pos=VB wnsn=1 the cmd=ignore pos=DT hotel cmd=done lemma=hotel lexsn=1:06:00:: pos=NN wnsn=1 and cmd=ignore pos=CC faced cmd=done lemma=face lexsn=2:42:02:: pos=VB wnsn=4 Summers cmd=done lemma=person lexsn=1:03:00:: pn=person pos=NNP rdf=person wnsn=1 across cmd=ignore pos=IN the cmd=ignore pos=DT counter cmd=done lemma=counter lexsn=1:06:00:: pos=NN wnsn=1 . 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 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') ... # 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 sentence_polarity ----------------- 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', '.'] 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 >>> 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'] subjectivity ----------- 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', ...] 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[record.find('ps').text] += 1 >>> for item, count in redup_pos_freqdist.most_common(): ... print(item, count) 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', ...] twitter_samples --------------- 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 `tweets.docs()`. 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') ['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') [['RT', '@KirkKus', ':', 'Indirect', 'cost', 'of', 'the', 'UK', 'being', 'in', ...], ['VIDEO', ':', 'Sturgeon', 'on', 'post-election', 'deals', 'http://t.co/BTJwrpbmOY'], ...] 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 [, , , ...] 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] >>> 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 >>> 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 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') # doctest: +ELLIPSIS The `vnclass()` method also accepts "short" identifiers, such as '10.1': >>> verbnet.vnclass('10.1') # doctest: +ELLIPSIS 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() 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 concise 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) Example: It's raining. Syntax: LEX[it] LEX[[+be]] VERB Semantics: * 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] Semantics: * 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] Semantics: * 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 == [{'semantics': [{'arguments': [{'value': 'during(E)', 'type': 'Event'}, {'value': 'Weather_type', 'type': 'VerbSpecific'}, {'value': '?Theme', 'type': 'ThemRole'}], 'predicate_value': 'weather'}], 'example': "It's raining.", 'syntax': [{'pos_tag': 'LEX', 'modifiers': {'value': 'it', 'synrestrs': [], 'selrestrs': []}}, {'pos_tag': 'LEX', 'modifiers': {'value': '[+be]', 'synrestrs': [], 'selrestrs': []}}, {'pos_tag': 'VERB', 'modifiers': {'value': '', 'synrestrs': [], 'selrestrs': []}}], 'description': {'primary': 'Intransitive', 'secondary': 'Expletive Subject'}}, {'semantics': [{'arguments': [{'value': 'during(E)', 'type': 'Event'}, {'value': 'Weather_type', 'type': 'VerbSpecific'}, {'value': 'Theme', 'type': 'ThemRole'}], 'predicate_value': 'weather'}], 'example': "It's raining cats and dogs.", 'syntax': [{'pos_tag': 'LEX', 'modifiers': {'value': 'it', 'synrestrs': [], 'selrestrs': []}}, {'pos_tag': 'LEX', 'modifiers': {'value': '[+be]', 'synrestrs': [], 'selrestrs': []}}, {'pos_tag': 'VERB', 'modifiers': {'value': '', 'synrestrs': [], 'selrestrs': []}}, {'pos_tag': 'NP', 'modifiers': {'value': 'Theme', 'synrestrs': [], 'selrestrs': []}}], 'description': {'primary': 'NP', 'secondary': 'Expletive Subject, Theme Object'}}, {'semantics': [{'arguments': [{'value': 'during(E)', 'type': 'Event'}, {'value': 'Weather_type', 'type': 'VerbSpecific'}, {'value': 'Theme', 'type': 'ThemRole'}], 'predicate_value': 'weather'}], 'example': 'It was pelting with rain.', 'syntax': [{'pos_tag': 'LEX', 'modifiers': {'value': 'it[+be]', 'synrestrs': [], 'selrestrs': []}}, {'pos_tag': 'VERB', 'modifiers': {'value': '', 'synrestrs': [], 'selrestrs': []}}, {'pos_tag': 'PREP', 'modifiers': {'value': 'with', 'synrestrs': [], 'selrestrs': []}}, {'pos_tag': 'NP', 'modifiers': {'value': 'Theme', 'synrestrs': [], 'selrestrs': []}}], 'description': {'primary': 'PP', 'secondary': 'Expletive Subject, Theme-PP'}}] True Verbnet corpus lets us access thematic roles individually using `themroles()`. >>> themroles = verbnet.themroles('57') >>> themroles == [{'modifiers': [{'type': 'concrete', 'value': '+'}, {'type': 'force', 'value': '+'}], 'type': 'Theme'}] True 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'] 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', ...] >>> 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 [, , ...] >>> 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'] multext_east ------------ 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 # doctest: +ELLIPSIS >>> nltk.corpus.treebank # doctest: +ELLIPSIS >>> nltk.corpus.names # doctest: +ELLIPSIS >>> nltk.corpus.genesis # doctest: +ELLIPSIS >>> nltk.corpus.inaugural # doctest: +ELLIPSIS 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') >>> # 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.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 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: >>> import os >>> str(nltk.corpus.genesis.root).replace(os.path.sep,'/') # 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 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,'/') '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: >>> str(nltk.corpus.brown.abspath('ce06')).replace(os.path.sep,'/') # 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', ...] >>> 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: +SKIP 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')) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE ...... ... ... 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. 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, 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('\\\\','/')) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE [FileSystemPathPointer('.../corpora/brown/ca01'), FileSystemPathPointer('.../corpora/brown/ca02'), ...] >>> print(str(nltk.corpus.brown.abspaths('ce06')).replace('\\\\','/')) # doctest: +ELLIPSIS [FileSystemPathPointer('.../corpora/brown/ce06')] >>> print(str(nltk.corpus.brown.abspaths(['ce06', 'ce07'])).replace('\\\\','/')) # 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-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, '.*\.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', '.', ...] >>> 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]) ['rural.txt', 'science.txt'] ['PM', 'denies', 'knowledge', 'of', 'AWB', ... ['english-kjv.txt', 'english-web.txt', 'finnish.txt', ... ['In', 'the', 'beginning', 'God', 'created', 'the', ... ['1789-Washington.txt', '1793-Washington.txt', ... ['Fellow', '-', 'Citizens', 'of', 'the', 'Senate', ... ['1945-Truman.txt', '1946-Truman.txt', ... ['PRESIDENT', 'HARRY', 'S', '.', 'TRUMAN', "'", ... ['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) True >>> corpus.words() ['This', 'is', 'the', 'first', 'sentence', '.', ...] >>> 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]) >>> 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() # 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'] >>> print(repr(brown.root).replace('\\\\','/')) # doctest: +ELLIPSIS FileSystemPathPointer('.../corpora/brown') >>> brown.words() ['The', 'Fulton', 'County', 'Grand', 'Jury', ...] >>> 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] Concatenation ------------- 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 = f1.open(encoding='utf-8').read().split() >>> l2 = f2.open(encoding='utf-8').read().split() >>> l3 = f3.open(encoding='utf-8').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, encoding='utf-8') >>> l1 = f1.open(encoding='utf-8').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, encoding='utf-8') >>> l3 = f3.open(encoding='utf-8').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' % 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 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 io import StringIO, BytesIO >>> from nltk.data import SeekableUnicodeStreamReader >>> stream = BytesIO(b"""\ ... This is a test file. ... It is encoded in ascii. ... """.decode('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(repr(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 = BytesIO(b"""\ ... This is a test file. ... It is encoded in utf-16. ... """.decode('ascii').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' >>> int(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 = BytesIO(b"""\ ... This is a test file. ... It is encoded in utf-16. ... """.decode('ascii').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 = 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() 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' 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'] ['whitespace.'] [] >>> # Bug fixed in 5279: >>> f = StringIO(b"a b c)".decode('ascii')) >>> 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/13085') ['SNYDER', '&', 'lt', ';', 'SOI', '>', 'MAKES', ...] >>> 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 ?'), ...]