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Semantic similarity matching method and device and storage medium

A technology of semantic similarity and matching method, which is applied in the field of semantic similarity matching method, device and storage medium, can solve the problems of feature loss, close connection of residual blocks, and failure to capture semantic information, so as to enrich semantic features and solve Insufficient feature loss and inter-sentence interaction to solve the effect of network gradient disappearance

Active Publication Date: 2021-03-26
GUILIN UNIV OF ELECTRONIC TECH
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  • Application Information

AI Technical Summary

Problems solved by technology

Although the RE2 model uses the residual connection to deepen the network depth, the RE2 model residual connection uses the summation method, which does not make the output features of each residual block closely connected with the original features, which is easy to cause feature loss
In addition, for the information interaction between sentences, the attention mechanism is used for word-level interaction, so that the model only learns the similar semantic features between sentence pairs, and does not capture more semantic information of sentence pairs, such as difference semantic features. and key semantic features

Method used

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  • Semantic similarity matching method and device and storage medium
  • Semantic similarity matching method and device and storage medium
  • Semantic similarity matching method and device and storage medium

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Experimental program
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Effect test

Embodiment 1

[0071] like figure 1 As shown, a semantic similarity matching method includes the following steps:

[0072] Importing the first sample to be analyzed and the second sample to be analyzed, respectively performing word vectorization processing on the first sample to be analyzed and the second sample to be analyzed, to obtain the first word corresponding to the first sample to be analyzed a vector matrix and a second word vector matrix corresponding to the second sample to be analyzed;

[0073] Constructing a training network, performing feature fusion processing on the first word vector matrix and the second word vector matrix through the training network, to obtain the first fusion vector matrix corresponding to the first word vector matrix and the corresponding The second fusion vector matrix corresponding to the second word vector matrix;

[0074] performing vector conversion on the first fusion vector matrix and the second fusion vector matrix respectively, to obtain a fir...

Embodiment 2

[0080] A semantic similarity matching method, comprising the steps of:

[0081] Importing the first sample to be analyzed and the second sample to be analyzed, respectively performing word vectorization processing on the first sample to be analyzed and the second sample to be analyzed, to obtain the first word corresponding to the first sample to be analyzed a vector matrix and a second word vector matrix corresponding to the second sample to be analyzed;

[0082] Constructing a training network, performing feature fusion processing on the first word vector matrix and the second word vector matrix through the training network, to obtain the first fusion vector matrix corresponding to the first word vector matrix and the corresponding The second fusion vector matrix corresponding to the second word vector matrix;

[0083] performing vector conversion on the first fusion vector matrix and the second fusion vector matrix respectively, to obtain a first transformation vector matr...

Embodiment 3

[0096] A semantic similarity matching method, comprising the steps of:

[0097] Importing the first sample to be analyzed and the second sample to be analyzed, respectively performing word vectorization processing on the first sample to be analyzed and the second sample to be analyzed, to obtain the first word corresponding to the first sample to be analyzed a vector matrix and a second word vector matrix corresponding to the second sample to be analyzed;

[0098] Constructing a training network, performing feature fusion processing on the first word vector matrix and the second word vector matrix through the training network, to obtain the first fusion vector matrix corresponding to the first word vector matrix and the corresponding The second fusion vector matrix corresponding to the second word vector matrix;

[0099] performing vector conversion on the first fusion vector matrix and the second fusion vector matrix respectively, to obtain a first transformation vector matr...

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Abstract

The invention provides a semantic similarity matching method and device and a storage medium, and the method comprises the steps: importing a first to-be-analyzed sample and a second to-be-analyzed sample, and carrying out the word vectorization of the first to-be-analyzed sample and the second to-be-analyzed sample, obtaining a first word vector matrix corresponding to the first to-be-analyzed sample and a second word vector matrix corresponding to the second to-be-analyzed sample; and constructing a training network, and performing feature fusion processing on the first word vector matrix and the second word vector matrix through the training network to obtain a first fusion vector matrix corresponding to the first word vector matrix and a second fusion vector matrix corresponding to thesecond word vector matrix. According to the method, the problems of feature loss, insufficient sentence interaction and network gradient disappearance are solved, the semantic features of sentences are enriched, information interaction between sentences is more accurate and richer, and semantic information of more sentence pairs can be captured.

Description

technical field [0001] The present invention mainly relates to the technical field of language processing, in particular to a semantic similarity matching method, device and storage medium. Background technique [0002] Text matching is an important research field in natural language processing. In the text matching task, the model takes two text sequences as input and predicts the semantic relationship between them. It can be widely used in a variety of tasks, such as natural language reasoning, judging whether a hypothesis can be inferred from a premise, or determining whether two sentences express the same meaning in paraphrase recognition, and answer selection, etc. These applications can Considered as a specific form of the text similarity matching problem, the core problem of text matching is to model the correlation between two sentences. [0003] Nowadays, the most popular method for text matching is deep neural network, and the semantic similarity matching model b...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F40/30G06F40/284G06F40/194G06K9/62G06N3/04
CPCG06F40/30G06F40/194G06F40/284G06N3/045G06N3/044G06F18/214G06F18/253
Inventor 蔡晓东田文靖
Owner GUILIN UNIV OF ELECTRONIC TECH
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