A field entity attribute relation extraction method based on distance supervision

A technology of relationship extraction and entity attributes, applied in the fields of natural language processing and deep learning, can solve problems that cannot be directly applied to general fields, and achieve good generalization effects

Active Publication Date: 2019-03-01
KUNMING UNIV OF SCI & TECH
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Problems solved by technology

[0003] The present invention provides a method for extracting domain entity attribute relationship based on distance supervision, which is used to solve the problem that the existin

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  • A field entity attribute relation extraction method based on distance supervision
  • A field entity attribute relation extraction method based on distance supervision
  • A field entity attribute relation extraction method based on distance supervision

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Embodiment 1

[0055] Embodiment 1: as Figure 1-3 As shown, a method for extracting domain entity attribute relationship based on distance supervision, the specific steps of the method are as follows:

[0056] Step1, at first construct the Chinese domain knowledge base, and utilize the entity in the domain knowledge base to obtain the training corpus from the tourism domain text collection; The concrete steps of described Step1 are as follows:

[0057] Step1.1, learn from the structural characteristics of the Freebase knowledge base to construct a domain knowledge base of Chinese tourist attractions;

[0058] Step1.2. Use different crawler programs for different websites to crawl text information in the field of tourism from travel websites and encyclopedia entries to form a text collection in the field of tourism;

[0059] Step1.3. Use the method of distance supervision (Distant Supervision) to construct a set of relational examples, use the knowledge base to find out the sentences that a...

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Abstract

The invention relates to a field entity attribute relation extraction method based on distance supervision, belonging to the technical field of natural language processing and depth learning. The method inlcudes constructing a domain knowledge base of Chinese tourist attractions, through the Chinese encyclopedia website and tourism website to obtain a large number of tourism domain text collections, using the constructed tourism domain knowledge base of entity pairs to obtain the relational instance text collections from the tourism domain text collection; using the theme model keyword similarity calculation and keyword pattern matching to denoise; finally, using the training corpus which is composed of positive and negative data under each relationship, the part-of-speech feature, dependency feature and short syntax tree feature of the training corpus are extracted, and the three features are fused into a larger feature with more abundant semantic information, and then the relationship extraction model is trained. Experiments show that the F value of the fusion of the three features extracted from the de-noising training corpus is the highest and the extraction performance is thebest.

Description

technical field [0001] The invention relates to a method for extracting domain entity attribute relationship based on distance supervision, and belongs to the technical fields of natural language processing and deep learning. Background technique [0002] As the core task and important link of information extraction, entity relationship extraction can realize the identification of semantic relationship between entity pairs, and plays an important role in sentence semantic understanding and entity semantic knowledge base construction. The domain entity relationship extraction is an extension and supplement to the general domain relationship extraction. This task expands the more fine-grained knowledge in the specific field, and provides help for humans and computers to better understand natural language information. On the one hand, the domain-specific entity relationship extraction The domain knowledge base can be expanded, and on the other hand, it can make people more awar...

Claims

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Application Information

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IPC IPC(8): G06F16/36G06F16/35G06F17/27
CPCG06F40/211G06F40/247G06F40/295
Inventor 郭剑毅王斌余正涛线岩团王红斌毛存礼
Owner KUNMING UNIV OF SCI & TECH
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