Question

我有一些推文，我想分成几个字。大多数工作正常，除非人们组合如：trumpisamoron或makeamericagreatagain这样的字词。但是，还有password之类的内容，不应该分为pass和word。

我知道nltk包有一个punkt tokenizer模块，可以智能地分割句子。是否有类似的话语？即使它不在nltk包中？

注意：password -> pass + word的示例比分裂词问题少得多。

Answer 1

参考：我对另一个问题的回答 - Need to split #tags to text。

我所做的答案中的变化是：（1）获得WORDS的不同语料库和（2）添加def memo(f)以加快进程。您可能需要根据您正在使用的域添加/使用语料库。

from __future__ import division
from collections import Counter
import re, nltk
from datetime import datetime

WORDS = nltk.corpus.reuters.words() + nltk.corpus.words.words()
COUNTS = Counter(WORDS)

def memo(f):
    "Memoize function f, whose args must all be hashable."
    cache = {}
    def fmemo(*args):
        if args not in cache:
            cache[args] = f(*args)
        return cache[args]
    fmemo.cache = cache
    return fmemo

def pdist(counter):
    "Make a probability distribution, given evidence from a Counter."
    N = sum(counter.values())
    return lambda x: counter[x]/N

P = pdist(COUNTS)

def Pwords(words):
    "Probability of words, assuming each word is independent of others."
    return product(P(w) for w in words)

def product(nums):
    "Multiply the numbers together.  (Like `sum`, but with multiplication.)"
    result = 1
    for x in nums:
        result *= x
    return result

def splits(text, start=0, L=20):
    "Return a list of all (first, rest) pairs; start <= len(first) <= L."
    return [(text[:i], text[i:]) 
            for i in range(start, min(len(text), L)+1)]

@memo
def segment(text):
    "Return a list of words that is the most probable segmentation of text."
    if not text: 
        return []
    else:
        candidates = ([first] + segment(rest) 
                      for (first, rest) in splits(text, 1))
        return max(candidates, key=Pwords)

print segment('password')     # ['password']
print segment('makeamericagreatagain')     # ['make', 'america', 'great', 'again']
print segment('trumpisamoron')     # ['trump', 'is', 'a', 'moron']
print segment('narcisticidiots')     # ['narcistic', 'idiot', 's']

有时，如果单词被溢出到较小的标记中，那么单词在WORDS字典中不存在的可能性就会更高。

在最后一段中，它将narcisticidiots分为3个令牌，因为我们的idiots中没有令牌WORDS。

# Check for sample word 'idiots'
if 'idiots' in WORDS:
    print("YES")
else:
    print("NO")

您可以将新用户定义的字词添加到WORDS。

.
.
user_words = []
user_words.append('idiots')

WORDS+=user_words
COUNTS = Counter(WORDS)
.
.
.
print segment('narcisticidiots')     # ['narcistic', 'idiots']

为了更好的解决方案，你可以使用bigram / trigram。

更多示例：Word Segmentation Task

边界上的分词

1 个答案: