【摘 要】
：
A Bi-LSTM based encode/decode mechanism for named entity recognition was studied in this research.In the proposed mechanism,Bi-LSTM was used for encoding,an Attention method was used in the intermedia
【机 构】
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College of Informatics,Huazhong Agricultural University,Wuhan,China;Hubei Key Laboratory of Agricult
【出 处】
：
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
A Bi-LSTM based encode/decode mechanism for named entity recognition was studied in this research.In the proposed mechanism,Bi-LSTM was used for encoding,an Attention method was used in the intermediate layers,and an unidirectional LSTM was used as decoder layer.By using element wise product to modify the conventional decoder layers,the proposed model achieved better F-score,compared with other three baseline LSTM-based models.For the purpose of algorithm application,a case study of causal gene discovery in terms of disease pathway enrichment was designed.In addition,the causal gene discovery rate of our proposed method was compared with another baseline methods.The result showed that trigger genes detection eectively increase the performance of a text mining system for causal gene discovery.
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