捕获单词并重写

Question

用nlpnet(http://nilc.icmc.usp.br/nlpnet/index.html)做了一个单词分类器。 目标是使用给定的标记器仅单独提取单词。

响应代码

import nlpnet
import codecs
import itertools
TAGGER = nlpnet.POSTagger('pos-pt', language='pt')

def TAGGER_txt(text):
return (list(TAGGER.tag(text)))
with codecs.open('document.txt', encoding='utf8') as original_file:
with codecs.open('document_teste.txt', 'w') as output_file:
for line in original_file.readlines():
print (line)
words = TAGGER_txt(line)
all_words = list(itertools.chain(*words))
nouns = [word[0] for word in all_words if word[1]=='V']
print (nouns)

结果

O gato esta querendo comer o ratão 
['gato', 'ratão']

Answer 1

问题：...将包含特定 POS 的 N 次以上的句子转储到文件

---

注意：假设'document.txt'每行包含一个句子！

def is_worth_saving(tags, pos, pos_count):
"""
:param tags:        nlpnet tags from ONE Sentence
:param pos:         The POS to filter
:param pos_count:   Number of 'param pos'
:return:
True if 'tags' contain more than 'pos_count' occurrences of 'pos'
False otherwise
"""  
pos_found = 0
# Iterate tags
for word, _pos in tags:
if _pos == pos:
pos_found += 1
return pos_found >= pos_count
if __name__ == '__main__':
with open('document.txt') as in_fh, open('document_test.txt', 'w') as out_fh:
for sentence in in_fh:
print('Sentence:{}'.format(sentence[:-1]))
tags = TAGGER.tag(sentence)
# As your Example Sentence has only **2** Verbs,
# pass 'pos_count=2'
if is_worth_saving(tags[0], 'V', 2):
out_fh.write(sentence)
print (tags[0])

输出：

Sentence:O gato esta querendo comer o ratão
[(u'O', u'ART'), (u'gato', u'N'), (u'esta', u'PROADJ'), (u'querendo', u'V'), (u'comer', u'V'), (u'o', u'ART'), (u'rat', u'N')]

使用 Python 测试：3.4.2 和 2.7.9

Answer 2

我认为这可能是您需要的本质。请参阅编辑版本。

正如您在问题中所说，标记Sentence的结果类似于tagged.如果您只想从Sentence中获取名词，则可以使用nouns =之后的表达式恢复它们。

Sentence = " O gato esta querendo comer o rato "  
tagged = [('O', 'ADJ'), ('gato', 'N'), ('esta', 'V'), ('querendo', 'V'), ('comer', 'V'), ('o', 'ADJ'), ('rato', 'N')]
nouns = [t[0] for t in tagged if t[1]=='N']
print (nouns)

输出：

['gato', 'rato']

编辑：我不清楚你想要什么。这是另一种可能性。

• 我没有安装 nlpnet，因为那会是相当多的工作，我自己不会使用它。
• 我用TAGGER_txt模拟TAGGER.txt。
• 我已将编码更改为拉丁语-1。它用于标题和codecs.open。

.

# -*- coding: Latin-1 -*-
import codecs
import itertools
def TAGGER_txt(text): ## simulate TAGGER.txt
return [[(u'O', u'ART'), (u'gato', u'N'), (u'esta', u'PROADJ'), (u'querendo', u'V'), (u'comer', u'V'), (u'o', u'ART'), (u'ratão', u'N')]]
with codecs.open('document.txt', encoding='Latin-1') as original_file:
with codecs.open('document_test.txt', 'w') as output_file:
for line in original_file.readlines():
print (line)
words = TAGGER_txt(line)
all_words = list(itertools.chain(*words))
nouns = [word[0] for word in all_words if word[1]=='N']
print (nouns)

输出：

O gato esta querendo comer o ratão 
['gato', 'ratão']