Recurrent Neural CRF for Aspect Term Extraction with Dependency Transmission

摘要：

　　This paper presents a novel neural architecture for aspect term extraction in fine-grained sentiment computing area.In addition to amalgamating sequential features(character embedding,word embedding and POS tagging information),we train an end-to-end Recurrent Neural Networks(RNNs)with meticulously designed dependency transmission between recurrent units,thereby making it possible to learn structural syntactic phenomena.The experimental results show that incorporating these shallow semantic features improves aspect term extraction performance compared to a system that uses no linguistic information,demonstrating the utility of morphological information and syntactic structures for capturing the affinity between aspect words and their contexts.

关键词： Aspect term extraction Dependency transmission Recurrent neural networks CRF

作者: Lindong Guo Shengyi Jiang Wenjing Du Suifu Gan

作者单位: School of Information Science and Technology,Guangdong University of Foreign Studies,Guangzhou,China School of Information Science and Technology,Guangdong University of Foreign Studies,Guangzhou,China

会议类型: 国际会议

会议名称: 2018自然语言处理与中文计算国际会议(NLPCC2018)

会议地点: 呼和浩特

会议语种:英文

页码: 378-390

在线出版日期: 2018-08-26（万方平台首次上网日期，不代表论文的发表时间）