用Python进行自然语言处理-2. Accessing Text Corpora and Lexical Resources

1. 处理文本语料库

1.1 古腾堡语料库

这是一个电子书语料库，目前提供49,000本免费电子书。
我们可以看看nltk里集成了多少电子书：

>>> 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']

我们可以用处理text1…text10的方法去处理。包括len、concordance等等。
我们还可以用raw方法获得所有字符，用words方法获得所有单词，用sents方法获得所有句子。进而可以计算平均词长、平均句长、用词多样性。

>>> for fileid in gutenberg.fileids():...     num_chars = len(gutenberg.raw(fileid)) [1]...     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(round(num_chars/num_words), round(num_words/num_sents), round(num_words/num_vocab), fileid)...5 25 26 austen-emma.txt5 26 17 austen-persuasion.txt5 28 22 austen-sense.txt4 34 79 bible-kjv.txt5 19 5 blake-poems.txt4 19 14 bryant-stories.txt4 18 12 burgess-busterbrown.txt4 20 13 carroll-alice.txt5 20 12 chesterton-ball.txt5 23 11 chesterton-brown.txt5 18 11 chesterton-thursday.txt4 21 25 edgeworth-parents.txt5 26 15 melville-moby_dick.txt5 52 11 milton-paradise.txt4 12 9 shakespeare-caesar.txt4 12 8 shakespeare-hamlet.txt4 12 7 shakespeare-macbeth.txt5 36 12 whitman-leaves.txt

1.2 网络聊天语料库

从略

1.3 布朗语料库

这是一个分类语料库：

ID File Genre Description A16 ca16 news Chicago Tribune: Society Reportage B02 cb02 editorial Christian Science Monitor: Editorials C17 cc17 reviews Time Magazine: Reviews D12 cd12 religion Underwood: Probing the Ethics of Realtors E36 ce36 hobbies Norling: Renting a Car in Europe F25 cf25 lore Boroff: Jewish Teenage Culture G22 cg22 belles_lettres Reiner: Coping with Runaway Technology H15 ch15 government US Office of Civil and Defence Mobilization: The Family Fallout Shelter J17 cj19 learned Mosteller: Probability with Statistical Applications K04 ck04 fiction W.E.B. Du Bois: Worlds of Color L13 cl13 mystery Hitchens: Footsteps in the Night M01 cm01 science_fiction Heinlein: Stranger in a Strange Land N14 cn15 adventure Field: Rattlesnake Ridge P12 cp12 romance Callaghan: A Passion in Rome R06 cr06 humor Thurber: The Future, If Any, of Comedy

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可以用categories方法获得所有分类：

>>> from nltk.corpus import brown>>> brown.categories()['adventure', 'belles_lettres', 'editorial', 'fiction', 'government', 'hobbies','humor', 'learned', 'lore', 'mystery', 'news', 'religion', 'reviews', 'romance','science_fiction']>>> brown.words(categories='news')['The', 'Fulton', 'County', 'Grand', 'Jury', 'said', ...]>>> brown.words(fileids=['cg22'])['Does', 'our', 'society', 'have', 'a', 'runaway', ',', ...]>>> brown.sents(categories=['news', 'editorial', 'reviews'])[['The', 'Fulton', 'County'...], ['The', 'jury', 'further'...], ...]

利用布朗语料库我们可以研究不同文体之间的风格差异。比如，我们可以比较不同文体的情态动词的差异，这里用到了tabulate方法制作表格，也用到了条件概率分布方法ConditionalFreqDist获得不同文体的概率分布：

>>> cfd = nltk.ConditionalFreqDist(...           (genre, word)...           for genre in brown.categories()...           for word in brown.words(categories=genre))>>> genres = ['news', 'religion', 'hobbies', 'science_fiction', 'romance', 'humor']>>> modals = ['can', 'could', 'may', 'might', 'must', 'will']>>> cfd.tabulate(conditions=genres, samples=modals)                 can could  may might must will           news   93   86   66   38   50  389       religion   82   59   78   12   54   71        hobbies  268   58  131   22   83  264science_fiction   16   49    4   12    8   16        romance   74  193   11   51   45   43          humor   16   30    8    8    9   13

1.4 路透社语料库

方法同布朗语料库

1.5 总统就职演讲语料库

>>> from nltk.corpus import inaugural>>> inaugural.fileids()['1789-Washington.txt', '1793-Washington.txt', '1797-Adams.txt', ...]>>> [fileid[:4] for fileid in inaugural.fileids()]['1789', '1793', '1797', '1801', '1805', '1809', '1813', '1817', '1821', ...]

根据文件名以“年份-总统名”命名的特点，我们可以计算不同时期，某些词的频度分布：

>>> cfd = nltk.ConditionalFreqDist(...           (target, fileid[:4])...           for fileid in inaugural.fileids()...           for w in inaugural.words(fileid)...           for target in ['america', 'citizen']...           if w.lower().startswith(target)) [1]>>> cfd.plot()