该名词和动词的位置

Question

我有一个基于规则的代码，可以打印出句子中的名词后跟动词

for text_id, text in enumerate(news_df['news_title'].values):
    
    # Remove the comma and full stops
    text = text.replace(',', '').replace('.', '').replace('-','')
    sentence_tags = POSTAG(text.lower())
    
    print(text)
    
    # Sentences parts
    for index, part in enumerate(sentence_tags):
        try:
            
            if 'NN' in part[1] and 'VB' in sentence_tags[index + 1][1]:
            print(">", part[0])
            break
            
        elif 'NN' in part[1] and 'NN' in sentence_tags[index + 1][1] and 'VB' in sentence_tags[index + 2][1]:
            print(">", part[0],  sentence_tags[index + 1][0])
            break
            
        elif 'NN' in part[1] and 'NN' in sentence_tags[index + 1][1] and 'NN' in sentence_tags[index + 2][1] and 'VB' in sentence_tags[index + 3][1]:
            print(">", part[0],  sentence_tags[index + 1][0], sentence_tags[index + 2][0])
            break

        except:
            pass
    print()

输出遵循这条规则的句子：

高中橄榄球运动员在视频表面出现欺凌行为后被起诉

> school football players

特朗普原告推动纽约通过成年幸存者法案计划起诉

>trump accuser

有没有办法同时打印出根据规则打印的那个名词的位置？例如：

>trump accuser , [0,5,"NN"] , [6,13,"VB"]

Answer 1

我更改了脚本并分隔了 state machine 段。这个程序 IMO 最严重的问题是它只是返回第一个模式（你可以很快修复它）。

import pandas as pd
import nltk
POSTAG = nltk.pos_tag
df = pd.DataFrame({'text':['high school football players charged after video surfaces showing hazing', 'trump accuser pushes new york to pass the adult survivors act plans to sue']})
for text_id, text in enumerate(df['text'].values):
    
    # Remove the comma and full stops
    text = text.replace(',', '').replace('.', '').replace('-','')
    tokens = nltk.word_tokenize(text.lower())
    sentence_tags = POSTAG(tokens)
    words = [item[0] for item in sentence_tags]
    start_end = []
    temp = 0
    for word in words:
      start_end.append([temp, temp+len(word)])
      temp+= (len(word)+1) 
    tags = [item[1] for item in sentence_tags]
    words_to_print = []
    tags_to_print = []
    start_end_to_print = []
    # the state machine 
    verb = False
    first_noun = False
    second_noun = False
    third_noun = False
    for w, t, se in zip(words, tags, start_end):
      if t.startswith('NN'):
        words_to_print.append(w)
        tags_to_print.append(t)
        start_end_to_print.append(se)
        first_noun = True

      elif t.startswith('NN') and first_noun:
        words_to_print.append(w)
        tags_to_print.append(t)
        start_end_to_print.append(se)
        second_noun = True

      elif t.startswith('NN') and second_noun:
        words_to_print.append(w)
        tags_to_print.append(t)
        start_end_to_print.append(se)
        third_noun = True

      elif t.startswith('VB') and (first_noun or second_noun or third_noun):
        break 
      
      elif (first_noun or second_noun or third_noun):
        words_to_print = []
        tags_to_print = []
        start_end_to_print = []
        verb = False
        first_noun, second_noun, third_noun = False, False, False
    
    print('> ', ' '.join(words_to_print), ' '.join([str(item[0])+' '+str(item[1]) for item in zip(start_end_to_print, tags_to_print)]))

输出：

>  school football players [5, 11] NN [12, 20] NN [21, 28] NNS
>  trump accuser [0, 5] NN [6, 13] NN

该名词和动词的位置

Position of that Noun and Verb

python

nlp

nltk

pos-tagger