Intelligent semantic matching method and device based on deep hierarchical coding

Abstract

The invention discloses an intelligent semantic matching method and device based on deep hierarchical coding, andd belongs to the technical field of artificial intelligence and natural language processing. The technical problem to be solved by the invention is how to capture more semantic context information and interaction information between sentences to achieve Intelligent semantic matching ofsentences; the adopted technical scheme is as follows: the method comprises the following steps: constructing and training a sentence matching model consisting of an embedding layer, a deep hierarchical coding representation layer, a hierarchical feature interaction matching layer and a prediction layer; according to the method, deep hierarchical coding representation of the sentences is realized,so that more semantic context information and interaction information between the sentences are obtained, and a hierarchical feature interaction matching mechanism is realized to achieve the purposeof intelligent semantic matching of the sentences. The device comprises a sentence matching knowledge base construction unit, a training data set generation unit, a sentence matching model construction unit and a sentence matching model training unit.

Description

technical field [0001] The invention relates to the technical fields of artificial intelligence and natural language processing, in particular to an intelligent semantic matching method and device based on deep layered coding. Background technique [0002] In recent years, semantic matching methods for sentences have received more and more attention in the field of natural language processing. The reason is that many natural language processing tasks are based on the semantic matching of sentences, which can be regarded as an extension of sentence semantic matching tasks to a certain extent. For example, the task of "automatic question answering" can be processed by calculating the matching degree of "question" and "candidate answer"; the task of "information retrieval" can be regarded as calculating the matching degree of "query sentence" and "matching document". Because of this, semantic matching of sentences plays a crucial role in the field of natural language processin...

AI Technical Summary

Problems solved by technology

[0003] Through analysis and research, it is not difficult to find that most of the existing technologies are based on convolutional neural network models or cyclic neural network models, and the characteristics and limitations of these two models make it impossible to completely solve this problem

Among them, although the convolutional neural network is good at capturing and representing local features with different kernel functions, it ignores the sequence information in the text and is not suitable for processing sequence information tasks; although the recurrent neural network can process sequence information, most of them only Generate the final vector representation without considering the hierarchical relationship of the sentence, which may lose some important intermediate encoding information

For the semantic matching task of sentences, the order of words in the sentence and the hierarchical information of the sentence are crucial, therefore, it is almost impossible to obtain satisfactory results by simply using the method based on the convolutional neural network model or the recurrent neural network model.

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