【摘 要】
：
Relation extraction is an important part of many information extrac-tion systems that mines structured facts from texts.Recently,deep learning has achieved good results in relation extraction.Attentio
【机 构】
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School of Computer and Information Technology,Beijing Jiaotong University
【出 处】
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第十七届全国计算语言学学术会议暨第六届基于自然标注大数据的自然语言处理国际学术研讨会(CCL 2018)
论文部分内容阅读
Relation extraction is an important part of many information extrac-tion systems that mines structured facts from texts.Recently,deep learning has achieved good results in relation extraction.Attention mechanism is also gradu-ally applied to networks,which improves the performance of the task.However,the current attention mechanism is mainly applied to the basic features on the lexical level rather than the higher overall features.In order to obtain more infor-mation of high-level features for relation predicting,we proposed attention-based piecewise convolutional neural networks(PCNN_ATT),which add an attention layer after the piecewise max pooling layer in order to get significant information of sentence global features.Furthermore,we put forward a data extension method by utilizing an external dictionary HIT IR-Lab Tongyici Cilin(Extended).Ex-periments results on ACE-2005 and COAE-2016 Chinese datasets both demon-strate that our approach outperforms most of the existing methods.
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