Semantic similarity feature extraction method based on double selection gates

A technology of semantic similarity and feature extraction, applied in semantic analysis, natural language data processing, instruments, etc., can solve the problems that users cannot quickly find keyword-related information, unsatisfactory search result quality, and information error matching, etc. Achieve the effect of solving the problem of network gradient disappearance and explosion, avoiding the influence of semantic similarity judgment, and improving matching efficiency
CN110765755APending Publication Date: 2020-02-07GUILIN UNIV OF ELECTRONIC TECH
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Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Applications(China)
Current Assignee / Owner
GUILIN UNIV OF ELECTRONIC TECH
Publication Date
2020-02-07

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Abstract

The invention discloses a semantic similarity feature extraction method based on double selection gates, and relates to the field of natural language processing. The technical scheme is as follows: firstly, peforming word segmentation and vectorization representation on an input sentence pair to obtain a word vector; inputting the obtained word vector sequence into a bidirectional long-short-termmemory network; obtaining contextual information vectors of the two sentences; secondly, obtaining core feature vectors of sentence pairs through double selection gates respectively; obtaining vectorsof a sentence pair, then inputting the vectors into a multi-angle semantic feature matching network to obtain feature matching vectors of the sentence pair, finally, combining the two semantic feature matching vectors of the matching vectors through a bidirectional long-short-term memory network aggregation layer, and predicting the similarity of the sentence pair. The method effectively alleviates the problem of low matching efficiency caused by information redundancy, and avoids the cost problem of manual core information extraction.
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Description

technical field

[0001] The invention relates to the field of natural language processing, in particular to a semantic similarity feature extraction method based on double selection gates. Background technique

[0002] Today's world is flooded with massive amounts of information, most of which are stored in the form of text, and an important topic of artificial intelligence is to organize and "express" these text information, so that computers can "understand" like humans these messages. Because there are many words in the language that have multiple meanings, and the same concept can be expressed in different ways, there are many uncertain factors. The traditional text similarity calculation method based on string matching is widely used in search engines and question answering systems. It has been difficult to meet the needs of users. When users enter keywords to find information that matches the keywords, the content returned by the search may correspond to content that d...

Claims

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