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python - 将 NLP WordNetLemmatizer 应用于整个句子显示错误且位置未知

问题描述

我想在整个句子上应用 NLP WordNetLemmatizer。问题是我得到一个错误：

KeyError: 'NNP'

就像我得到未知的'pos'值，但我不知道为什么。我想获得单词的基本形式，但没有'pos'它不起作用。你能告诉我我做错了什么吗？

import nltk

from nltk.tokenize import PunktSentenceTokenizer
from nltk.tokenize import word_tokenize
from nltk.tokenize import RegexpTokenizer
from nltk.stem import WordNetLemmatizer 

nltk.download('averaged_perceptron_tagger')

sentence = "I want to find the best way to lemmantize this sentence so that I can see better results of it"

taged_words = nltk.pos_tag(sentence)
print(taged_words)


lemmantised_sentence = []

lemmatizer = WordNetLemmatizer()
for word in taged_words:

     filtered_text_lemmantised =  lemmatizer.lemmatize(word[0], pos=word[1])
     print(filtered_text_lemmantised)

     lemmantised_sentence.append(filtered_text_lemmantised)

lemmantised_sentence = ' '.join(lemmantised_sentence)
print(lemmantised_sentence)

标签： pythonnlpnltk

解决方案

句子在发送到 pos_tag 函数之前应该被拆分。此外， pos 参数的不同之处在于它接受的字符串类型。它只接受'N'，'V'等。我已从此https://stackoverflow.com/a/15590384/7349991更新了您的代码。

import nltk

from nltk.tokenize import PunktSentenceTokenizer
from nltk.tokenize import word_tokenize
from nltk.tokenize import RegexpTokenizer
from nltk.stem import WordNetLemmatizer
from nltk.corpus import wordnet

def main():
    nltk.download('averaged_perceptron_tagger')
    nltk.download('wordnet')

    sentence = "I want to find the best way to lemmantize this sentence so that I can see better results of it"

    taged_words = nltk.pos_tag(sentence.split())
    print(taged_words)

    lemmantised_sentence = []


    lemmatizer = WordNetLemmatizer()
    for word in taged_words:
        if word[1]=='':
            continue
        filtered_text_lemmantised = lemmatizer.lemmatize(word[0], pos=get_wordnet_pos(word[1]))
        print(filtered_text_lemmantised)

        lemmantised_sentence.append(filtered_text_lemmantised)

    lemmantised_sentence = ' '.join(lemmantised_sentence)
    print(lemmantised_sentence)

def get_wordnet_pos(treebank_tag):

    if treebank_tag.startswith('J'):
        return wordnet.ADJ
    elif treebank_tag.startswith('V'):
        return wordnet.VERB
    elif treebank_tag.startswith('N'):
        return wordnet.NOUN
    else:
        return wordnet.ADV


if __name__ == '__main__':
    main()

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