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Conceptual interpretation extraction method based on semantic role annotation and application

A technology of semantic role labeling and concept, applied in the field of concept interpretation and extraction based on semantic role labeling, can solve problems such as time-consuming and labor-intensive, and achieve the effect of improving accuracy

Pending Publication Date: 2022-07-29
天津爱数智诚信息技术有限公司
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Problems solved by technology

This method is time-consuming and labor-intensive, so there is an urgent need for a method that can automatically extract concept explanation information from text quickly and accurately

Method used

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  • Conceptual interpretation extraction method based on semantic role annotation and application
  • Conceptual interpretation extraction method based on semantic role annotation and application
  • Conceptual interpretation extraction method based on semantic role annotation and application

Examples

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Embodiment 1

[0035] This embodiment provides a concept interpretation extraction method based on semantic role annotation, such as figure 1 shown, including the following steps:

[0036] Step S101, acquiring the text to be processed.

[0037] Step S102, segment the to-be-processed text into sentences based on the set characters.

[0038] The setting characters adopted in the specific implementation manner include ".", ".", "?", "?", "!", "!", "\n", "\s" and so on.

[0039] Step S103 , extracting the character feature part of each sentence by using the semantic role labeling method to form an initial concept and explanation.

[0040] Semantic Role Labeling (SRL) is a shallow semantic analysis technology, which uses sentences as units to analyze the predicate-argument structure of sentences. Its theoretical basis comes from the case grammar proposed by Fillmore (1968). In-depth analysis of the contained semantic information. Specifically, the task of semantic role labeling is to focus on...

Embodiment 2

[0090] This embodiment provides the application of the concept interpretation extraction method based on semantic role annotation as described above in Rest services. Specifically, the concept interpretation extraction method is used as a library, which can be called by users using Rest api, such as Image 6 shown.

[0091] This embodiment uses Python3 to program this service, based on the tornado framework as the basic framework of the Rest service, integrates concept and explanation extraction into the service as a library, and provides Rest Api. The specific interface design is shown in Table 3.

[0092] table 3

[0093]

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Abstract

The invention relates to a semantic role annotation-based concept interpretation extraction method and application, and the method comprises the following steps: obtaining a to-be-processed text, and segmenting the to-be-processed text into sentences; extracting a role feature part of each sentence through a semantic role labeling method to form an initial concept and explanation; performing matching processing on the initial concept and the explanation based on a pre-constructed part-of-speech syntactic rule tree to obtain a final concept and an explanation conforming to a rule; the invention further provides application of the concept interpretation extraction method based on semantic role labeling in Rest service. Compared with the prior art, the method has the advantages of automation, rapidness, accuracy and the like.

Description

technical field [0001] The invention belongs to the technical fields of text analysis, natural language processing and the like, and relates to a text information extraction method, in particular to a concept interpretation extraction method and application based on semantic role labeling. Background technique [0002] Knowledge Graph is an important branch technology of artificial intelligence. The construction of knowledge graph is to store things in the real world with rich semantics, specific logical meanings and rules. Therefore, when parsing natural language, it gets rid of the purely statistical representation of words and words. The key to enriching semantics is to add more information around concepts, including attributes, synonyms, subordinates, and triplet relationships of other entities. The description of the concept or the explanation of the concept can be described in one sentence. For example, "Mathematics is a discipline that uses symbolic language to stu...

Claims

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Application Information

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IPC IPC(8): G06F40/30G06F40/211G06F40/289G06F40/117
CPCG06F40/30G06F40/211G06F40/289G06F40/117
Inventor 李宁宁
Owner 天津爱数智诚信息技术有限公司
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