Relation extraction method in combination with clause-level remote supervision and semi-supervised ensemble learning

Abstract

The invention discloses a relation extraction method in combination with clause-level remote supervision and semi-supervised ensemble learning. The method is specifically implemented by the following steps of 1, aligning a relation triple in a knowledge base to a corpus library through remote supervision, and establishing a relation instance set; 2, removing noise data in the relation instance set by using syntactic analysis-based clause identification; 3, extracting morphological features of relation instances, converting the morphological features into distributed representation vectors, and establishing a feature data set; and 4, selecting all positive example data and a small part of negative example data in the feature data set to form a labeled data set, forming an unlabelled data set by the rest of negative example data after label removal, and training a relation classifier by using a semi-supervised ensemble learning algorithm. According to the method, the relation extraction is carried out in combination with the clause identification, the remote supervision and the semi-supervised ensemble learning; and the method has wide application prospects in the fields of automatic question-answering system establishment, massive information processing, knowledge base automatic establishment, search engines, specific text mining and the like.

Description

technical field [0001] The invention relates to the field of information extraction, in particular to a relation extraction method combining clause-level remote supervision and semi-supervised integrated learning. Background technique [0002] Information Extraction refers to the process of extracting information such as entities, events, and relationships from a piece of text, forming structured data and storing it in a database for user query and use. Relation Extraction is the key content of information extraction, which aims to extract the semantic relationship between entities. Relation extraction technology has broad application prospects in the fields of automatic question answering system construction, massive information processing, automatic knowledge base construction, search engines, and specific text mining. [0003] Traditional relation extraction research generally adopts supervised machine learning methods, which regard relation extraction as a classificatio...

AI Technical Summary

Problems solved by technology

[0007] In order to solve the problem of noise data and negative example data in the relation extraction method, the present invention provides a relation extraction method combining sentence-level remote supervision and semi-supervised ensemble learning, which can not only remove noise data, but also make full use of negative example data

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