zip與enumerate
words = ['I', 'turned', 'off', 'the', 'spectroroute']
tags = ['noun', 'verb', 'prep', 'det', 'noun']
print(list(zip(words, tags))) #[('I', 'noun'), ('turned', 'verb'), ('off', 'prep'), ('the', 'det'), ('spectroroute', 'noun')]
print(list(enumerate(words))) #[(0, 'I'), (1, 'turned'), (2, 'off'), (3, 'the'), (4, 'spectroroute')]
filter與map
def is_content_word(word):
return word.lower() not in ['a', 'of', 'the', 'and', 'will', ',', '.']
sent = ['Take', 'care', 'of', 'the', 'sense', ',', 'and', 'the',... 'sounds', 'will', 'take', 'care', 'of', 'themselves', '.']
print(list(filter(is_content_word, sent))) #['Take', 'care', 'sense', 'sounds', 'take', 'care', 'themselves']
lengths = list(map(len, nltk.corpus.brown.sents(categories='news')))
print(sum(lengths) / len(lengths))
詞性標(biāo)注
text = word_tokenize("And now for something completely different")
nltk.pos_tag(text) #[('And', 'CC'), ('now', 'RB'), ('for', 'IN'), ('something', 'NN'),('completely', 'RB'), ('different', 'JJ')]
從有標(biāo)記文本中分割出詞和詞性
>>> tagged_token = nltk.tag.str2tuple('fly/NN')
>>> tagged_token
('fly', 'NN')
>>> tagged_token[0]
'fly'
>>> tagged_token[1]
'NN'
>>> sent = '''
... The/AT grand/JJ jury/NN commented/VBD on/IN a/AT number/NN of/IN
... other/AP topics/NNS ,/, AMONG/IN them/PPO the/AT Atlanta/NP and/CC
... Fulton/NP-tl County/NN-tl purchasing/VBG departments/NNS which/WDT it/PPS
... said/VBD ``/`` ARE/BER well/QL operated/VBN and/CC follow/VB generally/RB
... accepted/VBN practices/NNS which/WDT inure/VB to/IN the/AT best/JJT
... interest/NN of/IN both/ABX governments/NNS ''/'' ./
.... '''
>>> [nltk.tag.str2tuple(t) for t in sent.split()]
[('The', 'AT'), ('grand', 'JJ'), ('jury', 'NN'), ('commented', 'VBD'),('on', 'IN'), ('a', 'AT'), ('number', 'NN'), ... ('.', '.')]
讀取內(nèi)置有標(biāo)記語料庫
>>> nltk.corpus.brown.tagged_words()
[('The', 'AT'), ('Fulton', 'NP-TL'), ...]
>>> nltk.corpus.brown.tagged_words(tagset='universal')
[('The', 'DET'), ('Fulton', 'NOUN'), ...]
依據(jù)上下文得到相似詞
text = nltk.Text(word.lower() for word in nltk.corpus.brown.words())
text.similar('bought') #made done put said found had seen given left heard been brought gotset was called felt in that told
帶有默認(rèn)值的字典default dictionary
>>> from collections import defaultdict
>>> frequency = defaultdict(int)
>>> frequency['colorless'] = 4
>>> frequency['ideas']
0
>>> pos = defaultdict(list)
>>> pos['sleep'] = ['NOUN', 'VERB']
>>> pos['ideas']
[]
