python自然言語処理学習ノート5-コーパスに載せる

gutenbergコーパス模様電子書籍

>>> import nltk
>>> nltk.corpus.gutenberg.fileids()
['austen-emma.txt', 'austen-persuasion.txt', 'austen-sense.txt', 'bible-kjv.txt', 'blake-poems.txt', 'bryant-stories.txt', 'burgess-busterbrown.txt', 'carroll-alice.txt', 'chesterton-ball.txt', 'chesterton-brown.txt', 'chesterton-thursday.txt', 'edgeworth-parents.txt', 'melville-moby_dick.txt', 'milton-paradise.txt', 'shakespeare-caesar.txt', 'shakespeare-hamlet.txt', 'shakespeare-macbeth.txt', 'whitman-leaves.txt']
>>> emma = nltk.corpus.gutenberg.words('austen-emma.txt')
>>> len(emma)
192427
#              （nltk.corpus.gutenberg.....）
#        
>>> from nltk.corpus import gutenberg

みそ紫̴̛ᴗ⁍̴̛⁎),私たちは使いやすいです.

>>> gutenberg.fileids()
['austen-emma.txt', 'austen-persuasion.txt', 'austen-sense.txt', 'bible-kjv.txt', 'blake-poems.txt', 'bryant-stories.txt', 'burgess-busterbrown.txt', 'carroll-alice.txt', 'chesterton-ball.txt', 'chesterton-brown.txt', 'chesterton-thursday.txt', 'edgeworth-parents.txt', 'melville-moby_dick.txt', 'milton-paradise.txt', 'shakespeare-caesar.txt', 'shakespeare-hamlet.txt', 'shakespeare-macbeth.txt', 'whitman-leaves.txt']
>>> for fileid in gutenberg.fileids():
...     num_chars = len(gutenberg.raw(fileid))
...     num_words = len(gutenberg.words(fileid))
...     num_sents = len(gutenberg.sents(fileid))
...     num_vocab = len(set([w.lower() for w in gutenberg.words(fileid)]))      ...     print (int(num_chars/num_words),int(num_words/num_sents),int(num_words/num_vocab),fileid)#    ，     ，     
... 

4 24 26 austen-emma.txt
4 26 16 austen-persuasion.txt
4 28 22 austen-sense.txt
4 33 79 bible-kjv.txt
4 19 5 blake-poems.txt
4 19 14 bryant-stories.txt
4 17 12 burgess-busterbrown.txt
4 20 12 carroll-alice.txt
4 20 11 chesterton-ball.txt
4 22 11 chesterton-brown.txt
4 18 10 chesterton-thursday.txt
4 20 24 edgeworth-parents.txt
4 25 15 melville-moby_dick.txt
4 52 10 milton-paradise.txt
4 11 8 shakespeare-caesar.txt
4 12 7 shakespeare-hamlet.txt
4 12 6 shakespeare-macbeth.txt
4 36 12 whitman-leaves.txt

例の中の関数を見てみましょう:raw(string):文章の原文

gutenberg.raw("austen-emma.txt")
'[Emma by Jane Austen 1816]

VOLUME I

CHAPTER I


Emma Woodhouse, handsome, clever, and rich, with a comfortable home
and happy disposition, seemed to unite some of the best blessings
of existence……

なんと生肉なんでしょう(￣Д￣)ﾉ
sents(string):テキストを文に変換し、各文は語鎖表である.

>>> gutenberg.sents('austen-emma.txt')
[['[', 'Emma', 'by', 'Jane', 'Austen', '1816', ']'], ['VOLUME', 'I'], ...]

words(string):分詞

>>> gutenberg.words('austen-emma.txt')
['[', 'Emma', 'by', 'Jane', 'Austen', '1816', ']', ...]

ネット文学ライブラリ

>>> from nltk.corpus import webtext
>>> webtext.fileids()
['firefox.txt', 'grail.txt', 'overheard.txt', 'pirates.txt', 'singles.txt', 'wine.txt']

ブラウンコーパス百万語級の英語電子コーパス:文体間の系統的な違いを研究する

>>> from nltk.corpus import brown
>>> brown.fileids()
['ca01', 'ca02', 'ca03', 'ca04', 'ca05', 'ca06', 'ca07', 'ca08', 'ca09', 'ca10', 'ca11', 'ca12', 'ca13', 'ca14', 'ca15', 'ca16', 'ca17', 'ca18', 'ca19', 'ca20', 'ca21', 'ca22', 'ca23', 'ca24'……

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

ロイターコーパスニュースドキュメント

>>> from nltk.corpus import reuters
>>> reuters.fileids()
['test/14826', 'test/14828', 'test/14829'……

就任演説コーパス

>>> from nltk.corpus import inaugural
>>> inaugural.fileids()
['1789-Washington.txt', '1793-Washington.txt', '1797-Adams.txt', '1801-Jefferson.txt', '1805-Jefferson.txt', '1809-Madison.txt'……
>>> [fileid[:4] for fileid in inaugural.fileids()]
['1789', '1793', '1797', '1801', '1805', '1809', '1813'……

自分のコーパスに読み込む

>>> from nltk.corpus import PlaintextCorpusReader
>>> corpus_root = 'usr/yourdictory'
>>> wordlists = PlaintextCorpusReader(corpus_root, '.*')