1 import nltk
2 from nltk.book import *
*** Introductory Examples for the NLTK Book ***
Loading text1, ..., text9 and sent1, ..., sent9
Type the name of the text or sentence to view it.
Type: 'texts()' or 'sents()' to list the materials.
text1: Moby Dick by Herman Melville 1851
text2: Sense and Sensibility by Jane Austen 1811
text3: The Book of Genesis
text4: Inaugural Address Corpus
text5: Chat Corpus
text6: Monty Python and the Holy Grail
text7: Wall Street Journal
text8: Personals Corpus
text9: The Man Who Was Thursday by G . K . Chesterton 1908
统计词语的数量
<Text: Wall Street Journal>
['Pierre',
'Vinken',
',',
'61',
'years',
'old',
',',
'will',
'join',
'the',
'board',
'as',
'a',
'nonexecutive',
'director',
'Nov.',
'29',
'.']
18
100676
12408
['bottom',
'Richmond',
'tension',
'limits',
'Wedtech',
'most',
'boost',
'143.80',
'Dale',
'refunded']
词频
1 dist = FreqDist(text7)
2 len(dist)
12408
1 vocab1 = dist.keys()
2 #vocab1[:10]
3 # In Python 3 dict.keys() returns an iterable view instead of a list
4 list(vocab1)[:10]
['Pierre', 'Vinken', ',', '61', 'years', 'old', 'will', 'join', 'the', 'board']
20
1 freqwords = [w for w in vocab1 if len(w) > 5 and dist[w] > 100]
2 freqwords
['billion',
'company',
'president',
'because',
'market',
'million',
'shares',
'trading',
'program']
标准化和词干
1 input1 = "List listed lists listing listings"
#把字母都小写，再进行分词处理
2 words1 = input1.lower().split(' ')
3 words1
['list', 'listed', 'lists', 'listing', 'listings']
1 porter = nltk.PorterStemmer()
2 [porter.stem(t) for t in words1]
['list', 'list', 'list', 'list', 'list']
词形还原
1 udhr = nltk.corpus.udhr.words('English-Latin1')
2 udhr[:20]
['Universal',
'Declaration',
'of',
'Human',
'Rights',
'Preamble',
'Whereas',
'recognition',
'of',
'the',
'inherent',
'dignity',
'and',
'of',
'the',
'equal',
'and',
'inalienable',
'rights',
'of']
1 [porter.stem(t) for t in udhr[:20]] # Still Lemmatization
['univers',
'declar',
'of',
'human',
'right',
'preambl',
'wherea',
'recognit',
'of',
'the',
'inher',
'digniti',
'and',
'of',
'the',
'equal',
'and',
'inalien',
'right',
'of']
1 WNlemma = nltk.WordNetLemmatizer()
2 [WNlemma.lemmatize(t) for t in udhr[:20]]
['Universal',
'Declaration',
'of',
'Human',
'Rights',
'Preamble',
'Whereas',
'recognition',
'of',
'the',
'inherent',
'dignity',
'and',
'of',
'the',
'equal',
'and',
'inalienable',
'right',
'of']
分词和分句
1 #根据空格分词
2 text11 = "Children shouldn't drink a sugary drink before bed."
3 text11.split(' ')
['Children', "shouldn't", 'drink', 'a', 'sugary', 'drink', 'before', 'bed.']
1 #nltk分词
2 nltk.word_tokenize(text11)
['Children',
'should',
"n't",
'drink',
'a',
'sugary',
'drink',
'before',
'bed',
'.']
1 #nltk分句
2 text12 = "This is the first sentence. A gallon of milk in the U.S. costs $2.99. Is this the third sentence? Yes, it is!"
3 sentences = nltk.sent_tokenize(text12)
4 len(sentences)
4
['This is the first sentence.',
'A gallon of milk in the U.S. costs $2.99.',
'Is this the third sentence?',
'Yes, it is!']
使用NLTK进行文本高级处理
POS标签
1 nltk.help.upenn_tagset('MD')
MD: modal auxiliary
can cannot could couldn't dare may might must need ought shall should
shouldn't will would
1 text13 = nltk.word_tokenize(text11)
2 nltk.pos_tag(text13)
[('Children', 'NNP'),
('should', 'MD'),
("n't", 'RB'),
('drink', 'VB'),
('a', 'DT'),
('sugary', 'JJ'),
('drink', 'NN'),
('before', 'IN'),
('bed', 'NN'),
('.', '.')]
1 text14 = nltk.word_tokenize("Visiting aunts can be a nuisance")
2 nltk.pos_tag(text14)
[('Visiting', 'VBG'),
('aunts', 'NNS'),
('can', 'MD'),
('be', 'VB'),
('a', 'DT'),
('nuisance', 'NN')]
1 # 解析语法结构
2 text15 = nltk.word_tokenize("Alice loves Bob")
3 grammar = nltk.CFG.fromstring("""
4 S -> NP VP
5 VP -> V NP
6 NP -> 'Alice' | 'Bob'
7 V -> 'loves'
8 """)
9
10 parser = nltk.ChartParser(grammar)
11 trees = parser.parse_all(text15)
12 for tree in trees:
13 print(tree)
(S (NP Alice) (VP (V loves) (NP Bob)))
1 #读取数据
2 text16 = nltk.word_tokenize("I saw the man with a telescope")
3 grammar1 = nltk.data.load('mygrammar.cfg')
4 grammar1
<Grammar with 13 productions>
1 #生成语法树
2 parser = nltk.ChartParser(grammar1)
3 trees = parser.parse_all(text16)
4 for tree in trees:
5 print(tree)
(S
(NP I)
(VP
(VP (V saw) (NP (Det the) (N man)))
(PP (P with) (NP (Det a) (N telescope)))))
(S
(NP I)
(VP
(V saw)
(NP (Det the) (N man) (PP (P with) (NP (Det a) (N telescope))))))
1 from nltk.corpus import treebank
2 text17 = treebank.parsed_sents('wsj_0001.mrg')[0]
3 print(text17)
(S
(NP-SBJ
(NP (NNP Pierre) (NNP Vinken))
(, ,)
(ADJP (NP (CD 61) (NNS years)) (JJ old))
(, ,))
(VP
(MD will)
(VP
(VB join)
(NP (DT the) (NN board))
(PP-CLR (IN as) (NP (DT a) (JJ nonexecutive) (NN director)))
(NP-TMP (NNP Nov.) (CD 29))))
(. .))
位置标记和歧义解释
1 text18 = nltk.word_tokenize("The old man the boat")
2 nltk.pos_tag(text18)
[('The', 'DT'), ('old', 'JJ'), ('man', 'NN'), ('the', 'DT'), ('boat', 'NN')]
1 text19 = nltk.word_tokenize("Colorless green ideas sleep furiously")
2 nltk.pos_tag(text19)
[('Colorless', 'NNP'),
('green', 'JJ'),
('ideas', 'NNS'),
('sleep', 'VBP'),
('furiously', 'RB')]