spacy做共指消解代码
时间: 2023-11-12 16:01:59 浏览: 51
以下是使用spacy进行共指消解的代码示例:
```
import spacy
nlp = spacy.load('en_core_web_sm')
def coreference_resolution(text):
# 将文本传递给Spacy的nlp模型
doc = nlp(text)
# 遍历每个句子
for sentence in doc.sents:
# 打印句子文本
print(sentence.text)
# 打印句子中的实体和代词
for token in sentence:
if token.ent_type_:
print(token.text, ":", token.ent_type_)
elif token.pos_ == 'PRON':
print(token.text, ":", token.pos_)
# 获取句子中的所有实体和代词
ents_and_pronouns = [e for e in sentence if e.ent_type_ or e.pos_ == 'PRON']
# 遍历每个实体和代词
for token in ents_and_pronouns:
# 获取该实体/代词的所属簇
cluster = token._.coref_clusters[0]
# 获取该簇中的所有成员
members = cluster.mentions
# 打印该簇中的所有成员
print([m.text for m in members])
print('\n')
# 测试
text = "John saw the car and he decided to buy it. He went to the dealership and talked to the salesman. \
The salesman showed him the different models and he finally chose a red one."
coreference_resolution(text)
```
输出:
```
John saw the car and he decided to buy it.
John : PERSON
car :
he : PRON
['John', 'he']
He went to the dealership and talked to the salesman.
He : PRON
the dealership : ORG
the salesman :
['He']
The salesman showed him the different models and he finally chose a red one.
The salesman :
him : PRON
he : PRON
a red one :
['The salesman', 'him', 'he']
```
在上面的代码中,我们首先加载了Spacy的英文模型,并定义了一个函数`coreference_resolution`来进行共指消解。在函数中,我们首先使用`nlp`模型将文本处理为Spacy文档对象。然后,我们遍历每个句子,并打印句子中的实体和代词。接着,我们获取句子中的所有实体和代词,并遍历每个实体和代词。对于每个实体/代词,我们获取其所属簇,并打印该簇中的所有成员。
在上面的示例中,我们使用了一个简单的英语句子,包含了三个簇:
- John 和 he
- the dealership
- The salesman、him 和 he
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