实体关系抽取解决了原始文本中目标实体之间的关系分类问题,同时也被广泛应用于文本摘要、自动问答系统、知识图谱、搜索引擎和机器翻译中。由于中文句式和语法结构复杂,并且汉语有更多歧义,会影响中文实体关系分类的效果。该文提出了基于多特征自注意力的实体关系抽取方法,充分考虑词汇、句法、语义和位置特征,使用基于自注意力的双向长短期记忆网络来进行关系预测。在中文COAE 2016 Task 3和英文SemEval 2010 Task 8 数据集上的实验表明该方法表现出了较好的性能。
Abstract
Entity relation extraction identifies the relation between the target entity in the raw text, wichi is also widely used in text summarization, automatic question answering system, knowledge map, search engine, and machine translation. To deal with the complex structure and ambiguity in the Chinese sentences, this paper proposes a multi-feature self-attention entity relation extraction method. It employ a self-attention-based Bi-LSTM to capture the lexical, syntactic, semantic and position features. The experimental results on the Chinese COAE-2016 Task 3 and the English SemEval-2010 Task 8 show our method produces better performances.
关键词
实体关系抽取 /
自注意力 /
双向长短期记忆网络 /
多特征
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Key words
entity relation extraction /
self-attention /
bidirectional long short-term memory /
multi-features
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参考文献
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脚注
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基金
国家自然科学基金(61363045)
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