将 WordNetLemmatizer.lemmatize() 与 pos_tags 一起使用会抛出 KeyError

Using WordNetLemmatizer.lemmatize() with pos_tags throws KeyError

我刚刚读到,在 pos_tags 的协助下,词形还原结果最好。因此,我遵循了下面的代码,但得到了计算 POS_tags 的 KeyError。下面是代码

   from nltk import pos_tag
   x['Phrase']=x['Phrase'].transform(lambda value:value.lower())
   x['Phrase']=x['Phrase'].transform(lambda value:pos_tag(value))

第 3 行后的输出(计算 POS 标签后)

   from nltk.stem import WordNetLemmatizer 
   lemmatizer = WordNetLemmatizer()
   x['Phrase_lemma']=x['Phrase'].transform(lambda value: ' '.join([lemmatizer.lemmatize(a[0],pos=a[1]) for a in  value]))

错误:

 KeyError                                  Traceback (most recent call last)
  <ipython-input-8-c2400a79a016> in <module>
  1 from nltk.stem import WordNetLemmatizer
  2 lemmatizer = WordNetLemmatizer()
  ----> 3 x['Phrase_lemma']=x['Phrase'].transform(lambda value: ' '.join([lemmatizer.lemmatize(a[0],pos=a[1]) for a in  value]))

 KeyError: 'DT'

你得到一个 KeyError 因为 wordnet 没有使用相同的 pos 标签。基于 source codewordnet 接受的 pos 标签是这些:adjadvadvverb

编辑 基于@bivouac0 的评论:

所以要绕过这个问题你必须制作一个映射器。映射功能很大程度上基于此answer。不支持的 POS 将不会被词形还原。

import nltk
import pandas as pd
from nltk.corpus import wordnet
from nltk.stem import WordNetLemmatizer 

lemmatizer = WordNetLemmatizer()

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
    elif treebank_tag.startswith('R'):
        return wordnet.ADV
    else:
        return None

x = pd.DataFrame(data=[['this is a sample of text.'], ['one more text.']], 
                 columns=['Phrase'])

x['Phrase'] = x['Phrase'].apply(lambda v: nltk.pos_tag(nltk.word_tokenize(v)))


x['Phrase_lemma'] = x['Phrase'].transform(lambda value: ' '.join([lemmatizer.lemmatize(a[0],pos=get_wordnet_pos(a[1])) if get_wordnet_pos(a[1]) else a[0] for a in  value]))