Keyword extracting method based on Word2Vec and Query log

Abstract

The invention discloses a keyword extracting method based on Word2Vec and a Query log, and relates to the field of information processing. The method includes the steps of S1, building a specific word list of a target field; S2, obtaining candidate keywords of documents in a document set; S3, obtaining word vectors of a plurality of dimensions of each candidate keyword; S4, calculating the cosine similarities between the word vectors of any candidate keyword L and a center vector, judging whether the candidate keyword L exists in the specific word list or not, if the candidate keyword L exists in the specific word list, directly implementing the step S5, and if the candidate keyword L does not exist in the specific word list, directly implementing the step S6; S5, multiplying the obtained cosine similarities by a weighting factor i to obtain new cosine similarities, and implementing the step S6; S6, ranking the values of the cosine similarities from large to small, outputting the values of m cosine similarities from the cosine similarity with the largest value, and obtaining final keywords. By means of the keyword extracting method, the keywords with the ideal quality can be rapidly and efficiently extracted for texts in specific fields, oral words are prevented from being introduced, and the extracted keywords are high in quality.

Description

technical field [0001] The invention relates to the field of information processing, in particular to a method for extracting keywords based on Word2Vec and Query log. Background technique [0002] Through document keywords, people can quickly understand the content of the text and grasp the theme of the document. Keywords are widely used in news reports, scientific papers and other fields to facilitate people to efficiently manage and retrieve documents. In addition to helping people quickly filter content of interest, document keywords can also be used in upper-level application areas such as search result ranking, text summarization, document classification, document clustering, and user modeling. [0003] Traditional keyword extraction methods are divided into two types, namely unsupervised methods and supervised methods. Among them, the unsupervised methods include TFIDF, Chi-squared, Text Rank, LDA and other methods, while the supervised method converts the keyword e...

AI Technical Summary

Problems solved by technology

Because of the high computational complexity, this algorithm is rarely used in large-scale text keyword extraction.

[0008] In summary, the traditional keyword extraction methods have the problems of poor keyword extraction effect, low keyword extraction efficiency and limited scope of application.

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