Question

from stemming.porter2 import stem

documents = ['got',"get"]

documents = [[stem(word) for word in sentence.split(" ")] for sentence in documents]
print(documents)

结果是：

[['got'], ['get']]

有人可以帮忙解释一下吗？谢谢！

Answer 1

您想要的是成词机而不是词干提取器。区别是微妙的。

通常，词干分析器会尽可能地删除后缀，并且在某些情况下，对于无法通过简单地删除后缀来查找归一化形式的单词，可以处理例外单词列表。

lemmatizer试图找到单词的“基本” /词根/不定式形式，通常，它需要针对不同语言的专门规则。

请参见

使用morphlem lemmatizer的NLTK实现进行词法化要求正确的词性（POS）标签相当准确。

避免（或实际上从不）尝试单独隔离词的词性。尝试对完全带有POS标签的句子进行词组化，例如

from nltk import word_tokenize, pos_tag
from nltk import wordnet as wn

def penn2morphy(penntag, returnNone=False, default_to_noun=False):
    morphy_tag = {'NN':wn.NOUN, 'JJ':wn.ADJ,
                  'VB':wn.VERB, 'RB':wn.ADV}
    try:
        return morphy_tag[penntag[:2]]
    except:
        if returnNone:
            return None
        elif default_to_noun:
            return 'n'
        else:
            return ''

使用penn2morphy helper函数，您需要将POS标签从pos_tag()转换为morphy标签，然后您可以：

>>> from nltk.stem import WordNetLemmatizer
>>> wnl = WordNetLemmatizer()
>>> sent = "He got up in bed at 8am."
>>> [(token, penn2morphy(tag)) for token, tag in pos_tag(word_tokenize(sent))]
[('He', ''), ('got', 'v'), ('up', ''), ('in', ''), ('bed', 'n'), ('at', ''), ('8am', ''), ('.', '')]
>>> [wnl.lemmatize(token, pos=penn2morphy(tag, default_to_noun=True)) for token, tag in pos_tag(word_tokenize(sent))]
['He', 'get', 'up', 'in', 'bed', 'at', '8am', '.']

为方便起见，您也可以尝试使用pywsd lemmatizer。

>>> from pywsd.utils import lemmatize_sentence
Warming up PyWSD (takes ~10 secs)... took 7.196984529495239 secs.
>>> sent = "He got up in bed at 8am."
>>> lemmatize_sentence(sent)
['he', 'get', 'up', 'in', 'bed', 'at', '8am', '.']

另请参阅https://stackoverflow.com/a/22343640/610569

为什么“获得”一词的词根仍然是“获得”而不是“获得”？

1 个答案: