Keyword extraction method and device based on graph and word and sentence collaboration

Abstract

The embodiment of the invention provides a keyword extraction method and device based on graph and word and sentence collaboration. The method comprises the following steps of: based on candidate words and sentences obtained from a text of a to-be-extracted keyword, calculating the diffusivity corresponding to each two candidate words; calculating a first weight of an edge between every two candidate words in the undirected weighted graph based on the diffusivity, calculating a first index of each candidate word based on the first weight; calculating a second weight of an edge between every two sentences in the directed weighted graph, calculating a second index of each sentence based on the second weight, and obtaining a first index vector of the sentence based on a second index of the sentence, constructing a second index vector of each candidate word containing a fifth index of the candidate word based on the first index of each candidate word and the obtained first index vector, and extracting a keyword in the text based on a size sequence of the fifth indexes in the second index vector. According to the embodiment of the invention, the accuracy of extracting the keywords in the text can be improved.

Description

technical field [0001] The present invention relates to the technical field of keyword extraction, in particular to a method and device for extracting keywords based on collaboration of graphs and words. Background technique [0002] Keywords are representative words in a piece of text, and are a brief summary of the topic of an article. Keywords can reflect the subject content of a document / text, and help people quickly locate the subject and thought of the document / text. In addition, keywords have important application value in literature retrieval, text classification, recommendation system and so on. Since it is very time-consuming and difficult to manually label keywords of documents / texts, automatic keyword extraction has become a hot research direction in the field of NLP (Natural Language Processing, Natural Language Processing). [0003] The existing method for extracting keywords from texts is: using the TextRank algorithm to extract keywords from texts. The imp...

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Problems solved by technology

However, in practical applications, the candidate word graph constructed by the TextRank algorithm has fewer edges and nodes, that is, fewer candidate words are selected, and it is only easy to extract candidate words with a high co-occurrence frequency (that is, high-frequency words) as keywords, which leads to the inaccuracy of the final extracted keywords

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