2

我有以下输入数据，我想从此输入中删除停用词并想做标记化：

input = [['Hi i am going to college', 'We will meet next time possible'],
         ['My college name is jntu', 'I am into machine learning specialization'],
         ['Machine learnin is my favorite subject' ,'Here i am using python for 
              implementation']]

我尝试了以下代码，但没有得到想要的结果：

from nltk.corpus import stopwords 
from nltk.tokenize import word_tokenize 
import nltk
nltk.download('stopwords')

stop_words = set(stopwords.words('english')) 

word_tokens = word_tokenize(input) 

filtered_sentence = [w for w in word_tokens if not w in stop_words] 

filtered_sentence = [] 

for w in word_tokens: 
    if w not in stop_words: 
        filtered_sentence.append(w) 

#print(word_tokens) 
print(filtered_sentence)

期望输出如下：

Output = [['Hi', 'going', 'college', 'meet','next', 'time', 'possible'],
          ['college', 'name','jntu', 'machine', 'learning', 'specialization'],
          ['Machine', 'learnin', 'favorite', 'subject' ,'using', 'python', 'implementation']]
4

2 回答 2

0

像以前一样开始

from nltk.corpus import stopwords 
from nltk.tokenize import word_tokenize 

stop_words = set(stopwords.words('english')) 

input_ = [['Hi i am going to college', 'We will meet next time possible'],
         ['My college name is jntu', 'I am into machine learning specialization'],
         ['Machine learnin is my favorite subject' ,'Here i am using python for implementation']]

我认为最好命名您的输入input_，因为input在 Python 中已经有意义。

我将从扁平化您的输入开始。我们应该有一个句子列表，而不是嵌套的列表列表：

input_flatten = [sentence for sublist in input for sentence in sublist]
print(input_flatten)
>>>['Hi i am going to college',
 'We will meet next time possible',
 'My college name is jntu',
 'I am into machine learning specialization',
 'Machine learnin is my favorite subject',
 'Here i am using python for implementation']

然后你可以遍历每个句子并删除停用词，如下所示：

sentences_without_stopwords = []
for sentence in input_flatten:
    sentence_tokenized = word_tokenize(sentence)
    stop_words_removed = [word for word in sentence_tokenized if word not in stop_words]
    sentences_without_stopwords.append(stop_words_removed)

print(sentences_without_stopwords)
>>>[['Hi', 'going', 'college'],
 ['We', 'meet', 'next', 'time', 'possible'],
 ['My', 'college', 'name', 'jntu'],
 ['I', 'machine', 'learning', 'specialization'],
 ['Machine', 'learnin', 'favorite', 'subject'],
 ['Here', 'using', 'python', 'implementation']]
于 2019-03-18T07:56:41.030 回答
0

我相信这会对你有所帮助。

stop_words = set(stopwords.words('english')) 
op=[]
for item in _input:
    word_tokens = word_tokenize(' '.join(item).lower()) 
    filtered_sentence = [w for w in word_tokens if not w in stop_words] 
    op.append(filtered_sentence)
print(op)

列表中的每个项目都有两个字符串。因此，将它们作为单个字符串加入并删除停用词。

于 2019-03-18T12:14:31.943 回答