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

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.txt 5 26 17 austen-persuasion.txt 5 28 22 austen-sense.txt 4 34 79 bible-kjv.txt 5 19 5 blake-poems.txt 4 19 14 bryant-stories.txt 4 18 12 burgess-busterbrown.txt 4 20 13 carroll-alice.txt 5 20 12 chesterton-ball.txt 5 23 11 chesterton-brown.txt 5 18 11 chesterton-thursday.txt 4 21 25 edgeworth-parents.txt 5 26 15 melville-moby_dick.txt 5 52 11 milton-paradise.txt 4 12 9 shakespeare-caesar.txt 4 12 8 shakespeare-hamlet.txt 4 12 7 shakespeare-macbeth.txt 5 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

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 couldmay might must will news9386663850389 religion825978125471 hobbies268581312283264 science_fiction1649412816 romance7419311514543 humor163088913

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()

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