使用Spacy在DOC中查找大多数类似的句子

Question

我正在寻找一种解决方案，以使用Gensim中的most_similar()，但使用Spacy。我想在使用NLP的句子列表中找到最相似的句子。

我尝试使用Spacy(例如https://spacy.io/api/doc#simarility(从循环中使用similarity()，但需要很长时间。

更深入：

我想将所有这些句子放在图中(像这样(以找到句子簇。

有什么想法？

Answer 1

这是一个简单的内置解决方案，您可以使用：

import spacy
nlp = spacy.load("en_core_web_lg")
text = (
    "Semantic similarity is a metric defined over a set of documents or terms, where the idea of distance between items is based on the likeness of their meaning or semantic content as opposed to lexicographical similarity."
    " These are mathematical tools used to estimate the strength of the semantic relationship between units of language, concepts or instances, through a numerical description obtained according to the comparison of information supporting their meaning or describing their nature."
    " The term semantic similarity is often confused with semantic relatedness."
    " Semantic relatedness includes any relation between two terms, while semantic similarity only includes 'is a' relations."
    " My favorite fruit is apples."
)
doc = nlp(text)
max_similarity = 0.0
most_similar = None, None
for i, sent in enumerate(doc.sents):
    for j, other in enumerate(doc.sents):
        if j <= i:
            continue
        similarity = sent.similarity(other)
        if similarity > max_similarity:
            max_similarity = similarity
            most_similar = sent, other
print("Most similar sentences are:")
print(f"-> '{most_similar[0]}'")
print("and")
print(f"-> '{most_similar[1]}'")
print(f"with a similarity of {max_similarity}")

(Wikipedia的文字(

它将产生以下输出：

Most similar sentences are:
-> 'Semantic similarity is a metric defined over a set of documents or terms, where the idea of distance between items is based on the likeness of their meaning or semantic content as opposed to lexicographical similarity.'
and
-> 'These are mathematical tools used to estimate the strength of the semantic relationship between units of language, concepts or instances, through a numerical description obtained according to the comparison of information supporting their meaning or describing their nature.'
with a similarity of 0.9583859443664551

请注意spacy.io的以下信息：

为了使它们紧凑且快速，Spacy的小管道包(所有以SM结尾的包装(不会与单词矢量发货，而仅包含上下文敏感的张量。这意味着您仍然可以使用相似性((方法来比较文档，跨度和令牌 - 但结果不会那么好，并且单个令牌不会分配任何向量。因此，为了使用真实单词向量，您需要下载较大的管道包：

- python -m spacy download en_core_web_sm
+ python -m spacy download en_core_web_lg

还有关如何提高相似性分数的建议，请参见Spacy vs Word2Vec中的文档相似性。