使用spacy文档相似度函数将数据集中的一个文档与所有其他文档进行比较的步骤如下:
import spacy
from spacy import displacy
from spacy.matcher import PhraseMatcher
nlp = spacy.load('en_core_web_sm')
matcher = PhraseMatcher(nlp.vocab)
other_documents = [...] # 其他文档的列表
for doc in other_documents:
doc = nlp(doc)
sentences = [sent.text for sent in doc.sents]
patterns = [nlp(sent) for sent in sentences]
matcher.add("Sentences", None, *patterns)
document_to_compare = "要比较的文档"
document_to_compare = nlp(document_to_compare)
sentences_to_compare = [sent.text for sent in document_to_compare.sents]
similar_sentences = []
for sentence in sentences_to_compare:
pattern = nlp(sentence)
matches = matcher(pattern)
similarities = []
for match_id, start, end in matches:
span = document_to_compare[start:end]
similarity = span.similarity(pattern)
similarities.append((span.text, similarity))
similarities.sort(key=lambda x: x[1], reverse=True)
similar_sentences.append(similarities[0][0])
for i, sentence in enumerate(sentences_to_compare):
print(f"句子 {i+1}:")
print("原始句子:", sentence)
print("相似句子:", similar_sentences[i])
print()
这样,你就可以使用spacy文档相似度函数将数据集中的一个文档与所有其他文档进行比较了。请注意,这只是一个基本的示例,你可以根据实际需求进行修改和扩展。
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