Keyword extracting method based on seq2seq (sequence to sequence) deep neural network model

Abstract

The invention relates to the field of computers, in particular to a keyword extracting method based on a seq2seq (sequence to sequence) deep neural network model. The keyword extracting method comprises the following steps: first, extracting target information through a pre-processing module; then, converting and labeling the target information through a word vector conversion module and a part-of-speech labeling module; next, obtaining a candidate word sequence through a candidate word weight calculating module; finally, obtaining a keyword through a candidate word screening module. Accordingto the keyword extracting method based on the seq2seq deep neural network model, the importance of each word to a document can be analyzed better and the keyword which can represent the theme of thedocument better is selected by regarding a document vector as the average of a word vector, and combining the word vector and the document vector to serve as the vector representation of a word; meanwhile, the investigation scope of keyword extracting is expanded; the defect that keywords beyond a vocabulary and the keywords which are not in the contents of a source document cannot be predicted byan existing extracting technology is overcome.

Description

technical field [0001] The invention relates to the field of computers, in particular to a keyword extraction method based on a seq2seq deep neural network model. Background technique [0002] With the development of computer and network technology and the advent of the era of big data, digital files are growing at an alarming rate, and a large amount of information that humans come into contact with exists in the form of electronic documents. Faced with such a vast amount of information, people urgently need machines that can automatically identify the keywords that best represent the main content of the article, help us understand the main content of the article faster, and save time in reading, processing and utilizing these electronic documents. [0003] These technologies are currently called Keyword Extraction (Keyword Extraction). Keyword extraction refers to quickly obtaining multiple words or phrases from a document that can represent the subject of the document as ...

AI Technical Summary

Problems solved by technology

[0006] In the search keywords, half of the keywords do not come from the source document, and the existing keyword extraction technology can only select candidate keywords from the source document, so it is impossible to predict the keywords that are not in the source document, and cannot The synonyms of words in the document are used as keywords, which greatly affects the accuracy of keyword extraction

[0007] At the same time, the existing keyword extraction technology can only select candidate words from a vocabulary of a certain size. When the size of document words far exceeds the size of the vocabulary, the inability to predict words outside the vocabulary will affect the accuracy of keyword extraction.

[0008] Existing keyword extraction methods usually consider the features obtained by machine learning when selecting candidate keywords. However, these features can only discover the importance of each word by counting the frequency of words in the document, and cannot reveal hidden keywords. full semantics in document content

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