Python 中使用 NLTK 的短语的索引

concordance for a phrase using NLTK in Python

是否可以在 NLTK 中获取短语的索引?

import nltk
from nltk.corpus import PlaintextCorpusReader

corpus_loc = "c://temp//text//"
files = ".*\.txt"
read_corpus = PlaintextCorpusReader(corpus_loc, files)
corpus  = nltk.Text(read_corpus.words())
test = nltk.TextCollection(corpus_loc)

corpus.concordance("claim")

比如上面的returns

on okay okay okay i can give you the claim number and my information and
 decide on the shop okay okay so the claim number is xxxx - xx - xxxx got

现在如果我尝试 corpus.concordance("claim number") 它不起作用...我确实有代码可以通过使用 .partition() 方法和一些相同的进一步编码来做到这一点...但我想知道是否可以使用 concordance.

来做同样的事情

根据这个 issue 还不能用 concordance() 函数搜索多个单词。

如果您阅读了@b3000 挖掘出的 issue 下的讨论,您会发现很奇怪,实际上可以使用多词索引——但只能在图形索引工具中使用,你可以这样启动:

>>> from nltk.app import concordance
>>> concordance()

我把这个解决方案放在一起...

def n_concordance_tokenised(text,phrase,left_margin=5,right_margin=5):
    #concordance replication via https://simplypython.wordpress.com/2014/03/14/saving-output-of-nltk-text-concordance/

    phraseList=phrase.split(' ')

    c = nltk.ConcordanceIndex(text.tokens, key = lambda s: s.lower())

    #Find the offset for each token in the phrase
    offsets=[c.offsets(x) for x in phraseList]
    offsets_norm=[]
    #For each token in the phraselist, find the offsets and rebase them to the start of the phrase
    for i in range(len(phraseList)):
        offsets_norm.append([x-i for x in offsets[i]])
    #We have found the offset of a phrase if the rebased values intersect
    #--
    # 
    #the intersection method takes an arbitrary amount of arguments
    #result = set(d[0]).intersection(*d[1:])
    #--
    intersects=set(offsets_norm[0]).intersection(*offsets_norm[1:])

    concordance_txt = ([text.tokens[map(lambda x: x-left_margin if (x-left_margin)>0 else 0,[offset])[0]:offset+len(phraseList)+right_margin]
                    for offset in intersects])

    outputs=[''.join([x+' ' for x in con_sub]) for con_sub in concordance_txt]
    return outputs

def n_concordance(txt,phrase,left_margin=5,right_margin=5):
    tokens = nltk.word_tokenize(txt)
    text = nltk.Text(tokens)

    return

n_concordance_tokenised(text,phrase,left_margin=left_margin,right_margin=right_margin)

n_concordance_tokenised(text1,'monstrous size')
>> [u'one was of a most monstrous size . ... This came towards ',
    u'; for Whales of a monstrous size are oftentimes cast up dead ']