Chinese question semantic comprehension method utilizing semantic dependency analysis

A technology of dependency analysis and semantic understanding, applied in semantic analysis, neural learning methods, natural language data processing, etc., can solve problems such as inability to cover the semantic relationship within arguments, complexity, and few syntax restrictions
CN112733547APending Publication Date: 2021-04-30BEIJING INST OF COMP TECH & APPL
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Patent Information

Authority / Receiving Office
CN · China
Current Assignee / Owner
BEIJING INST OF COMP TECH & APPL
Publication Date
2021-04-30

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Abstract

The invention relates to a Chinese question semantic comprehension method by utilizing semantic dependency analysis, which comprises the following steps of: step 1, collecting and sorting a question data set in an application field; step 2, preprocessing the obtained question data set; step 3, constructing a semantic dependency analysis model based on BiLSTM (Bidirectional Long Short Term Memory); step 4, constructing a semantic dependency structure analysis rule; and step 5, mapping the query triad list obtained by conversion in the step 4 into a basic diagram mode in the SPARQL statement to form a triad mode relationship. According to the method, through the semantic dependency analysis technology, in combination with the Chinese word segmentation technology, a new mode of efficient semantic understanding can be provided for users, and the method plays an important role in intelligent questioning and answering, search recommendation and other systems in the specific application field.
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Description

technical field

[0001] The invention relates to a computer semantic analysis technology, in particular to a method for understanding the semantics of informational Chinese question sentences using semantic dependency analysis. Background technique

[0002] Semantic understanding of sentences is an important research direction of natural language processing technology, and has been widely used in fields such as intelligent question answering. Due to the complexity of the Chinese language, it is difficult to understand questions and the progress is slow. At present, the semantic understanding methods of questions in intelligent question answering mainly include template method, keyword method, dependency parsing, semantic role labeling, etc.

[0003] The template method classifies the questions entered by the user through a classifier. If they can be classified into these categories, they can be correctly converted and processed according to the templates corresponding to eac...

Claims

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