基于最大熵的实体关系自动提取

Zhang Suxiang, Wen Juan, Wang Xiaojie, Li Lei
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引用次数: 5

摘要

实体关系抽取(Entity relation extraction, RE)是信息抽取中一个非常重要的研究领域,在本文中我们可以把实体关系抽取看作一个分类问题,在汉语中,实体关系抽取仍然是一个比较原始的研究领域,基于最大熵(maximum entropy, ME)的机器学习首次用于中文文本中命名实体之间的实体关系抽取,设计了13个特征用于实体关系抽取,包括形态学、语法和语义特征。构建了可再生能源的系统体系结构。实验表明,该方法具有良好的性能。因此,基于神经网络的机器学习是解决可重构问题的有效方法
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automatic Entity Relation Extraction Based on Maximum Entropy
Entity relation extraction (RE) is an very important research domain in information extraction, we can regard RE as a classification problem in this paper, RE is still original study field in Chinese language now, maximum entropy (ME)-based machine learning is the first time to be used to extract entity relations between named entities from Chinese texts, Thirteen features have been designed for entity relation extraction, which includes morphology, grammar and semantic feature. The system architecture for RE has been constructed. Experiment shows that the performance is promising. So it is useful for ME-based machine learning to solve RE problem
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