2

我正在尝试从我的数据集上的 textacy 中实现“extract.subject_verb_object_triples”功能。但是，我编写的代码非常缓慢且占用大量内存。有没有更有效的实现方式？

import spacy
import textacy

def extract_SVO(text):

    nlp = spacy.load('en_core_web_sm')
    doc = nlp(text)
    tuples = textacy.extract.subject_verb_object_triples(doc)
    tuples_to_list = list(tuples)
    if tuples_to_list != []:
        tuples_list.append(tuples_to_list)

tuples_list = []          
sp500news['title'].apply(extract_SVO)
print(tuples_list)

样本数据 (sp500news)

    date_publish  \
0       2013-05-14 17:17:05   
1       2014-05-09 20:15:57   
4       2018-07-19 10:29:54   
6       2012-04-17 21:02:54   
8       2012-12-12 20:17:56   
9       2018-11-08 10:51:49   
11      2013-08-25 07:13:31   
12      2015-01-09 00:54:17   

 title  
0       Italy will not dismantle Montis labour reform  minister                            
1       Exclusive US agency FinCEN rejected veterans in bid to hire lawyers                
4       Xis campaign to draw people back to graying rural China faces uphill battle        
6       Romney begins to win over conservatives                                            
8       Oregon mall shooting survivor in serious condition                                 
9       Polands PGNiG to sign another deal for LNG supplies from US CEO                    
11      Australias opposition leader pledges stronger economy if elected PM                
12      New York shifts into Code Blue to get homeless off frigid streets
4

1 回答 1

4

这应该会加快速度 -

import spacy
import textacy
nlp = spacy.load('en_core_web_sm')
def extract_SVO(text):
    tuples = textacy.extract.subject_verb_object_triples(text)
    if tuples:
        tuples_to_list = list(tuples)
        tuples_list.append(tuples_to_list)

tuples_list = []          
sp500news['title'] = sp500news['title'].apply(nlp)
_ = sp500news['title'].apply(extract_SVO)
print(tuples_list)

解释

在 OP 实现中，nlp = spacy.load('en_core_web_sm')从它每次加载的函数内部调用。我感觉这是最大的瓶颈。这可以取出，它应该加快速度。

此外，只有当元组不为空时，才会发生tuple转换。list

于 2018-12-27T13:22:09.427 回答