Research on Pattern Representation Method in Semi-supervised Semantic Relation Extraction Based on Bootstrapping

2014 Seventh International Symposium on Computational Intelligence and Design Pub Date : 2014-12-13 DOI:10.1109/ISCID.2014.154

Fei-yue Ye, Hao Shi, Shanpeng Wu

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引用次数: 6

Abstract

Semantic relation extraction is an important part of information extraction, it has application value in the automatic question answering system, retrieval system, ontology learning, semantic web annotation, and many other areas. Pattern representation method is context pattern in previous semi-Supervised semantic relation extraction based on bootstrapping, but it did not consider the role of the keywords in the semantic relation. This paper presents an improved context pattern, which has a stronger semantic expressiveness, which is used to extract semantic relations and makes the semantic relation extraction more accurate. First of all, the sentence context pattern is obtained by lexical analysis. Then, the syntax tree model is obtained by syntactic analysis, calculate words weight using the syntax tree pattern. Finally, extract semantic relations using semi-Supervised machine learning method based on bootstrapping. The experimental results show that this method can effectively extract the semantic relations.

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基于自举的半监督语义关系提取模式表示方法研究

语义关系抽取是信息抽取的重要组成部分，在自动问答系统、检索系统、本体学习、语义web标注等诸多领域都有应用价值。以往基于自举的半监督语义关系提取中的模式表示方法是上下文模式，但没有考虑关键词在语义关系中的作用。本文提出了一种改进的上下文模式，该模式具有更强的语义表达能力，可用于提取语义关系，提高了语义关系提取的准确性。首先，通过词法分析得出句子的语境模式。然后，通过句法分析得到句法树模型，利用句法树模式计算词权。最后，采用基于自举的半监督机器学习方法提取语义关系。实验结果表明，该方法可以有效地提取语义关系。

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2014 Seventh International Symposium on Computational Intelligence and Design

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