【摘 要】
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Relation extraction is an important semantic processing task in natu-ral language processing.The state-of-the-art systems usually rely on elaborately designed features,which are usually time-consuming
【机 构】
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Dalian University of Technology,Dalian,116024,China
【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 semantic processing task in natu-ral language processing.The state-of-the-art systems usually rely on elaborately designed features,which are usually time-consuming and may lead to poor gen-eralization.Besides,most existing systems adopt pipeline methods,which treat the task as two separated tasks,i.e.,named entity recognition and relation ex-traction.However,the pipeline methods suffer two problems:(1)Pipeline mod-el over-simplifies the task to two independent parts.(2)The errors will be ac-cumulated from named entity recognition to relation extraction.Therefore,we present a novel joint model for entities and relations extraction based on multi-head attention,which avoids the problems in the pipeline methods and reduces the dependence on features engineering.The experimental results show that our model achieves good performance without extra features.Our model reaches an F-score of 85.7%on SemEval-2010 relation extraction task 8,which has com-petitive performance without extra feature compared with previous joint mod-els.On publication,codes will be made publicly available.
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