Pattern self-learning based Chinese open relationship extraction method

Abstract

Open Chinese entity relationship extraction refers to, on the premise of not limiting a corpus field and a relationship category, automatic extraction of relationship information between entities from a Chinese corpus to obtain an entity relationship tuple. The present invention discloses a pattern self-learning based Chinese opening relationship extraction method. The method comprises the following three main steps of: firstly, based on an existing knowledge library, acquiring a high-quality entity relationship tuple and a corresponding sentence as a training corpus, and obtaining a dependent path mode between an entity and a relationship word by a pattern learning method proposed by the present invention; secondly, performing pre-processing of word segmentation, part-of-speech tagging, dependency analysis and the like on a to-be-extracted text, and performing entity relationship extraction by means of a relationship mode obtained by previous learning; and finally, performing quality evaluation on an entity relationship extracted automatically from the Chinese corpus by using a machine learning method, and obtaining the high-quality entity relationship tuple.

Description

technical field [0001] The invention relates to the field of natural language processing, in particular to Chinese information extraction and open Chinese relation extraction. Background technique [0002] Open relational extraction refers to automatically extracting entities and semantic relations between entities from text, without pre-defining relation types, and directly using words in the text as entity and relational words in relational tuples. For example, from the following example sentence "Obama graduated from Columbia University", the following ternary relational tuple can be extracted: (Obama, graduated, Columbia University). Open entity relationship extraction is the basis of knowledge base construction, and it has very important practical application value for intelligent information retrieval and application. [0003] Open relation extraction methods are mainly divided into three types, namely methods based on part of speech, methods based on semantic role la...

AI Technical Summary

Problems solved by technology

Some existing relational extraction methods based on dependency analysis mainly pre-define limited relational patterns and then extract relational tuples, so the recall rate of extracted relational tuples is difficult to meet the practical application

At the same time, there are also some open relational extraction systems that learn and use a large number of dependency relational patterns to extract relational tuples. However, it is easy to make mistakes when finding the sentences corresponding to relational tuples during the learning process, which reduces the accuracy of relational patterns.

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