Target Extraction via Feature-Enriched Neural Networks Model

摘要：

　　Target extraction is an important task in target-based sentiment analysis,which aims at identifying the boundary of target in given text.Previous works mainly utilize conditional random field(CRF)with a lot of handcraft features to recognize the target.However,it is hard to manually extract effective features to boost the performance of CRF-based methods.In this paper,we employ gated recurrent units(GRU)with label inference,to find valid label path for word sequence.At the same time,we find that character-level features play important roles in target extraction,and represent each word by concatenating word embedding and character-level representations which are learned via character-level GRU.Further,we capture boundary features of each word from its context words by convolution neural networks to assist the identification of the target boundary,since the boundary of a target is highly related to its context words.Experiments on two datasets show that our model outperforms CRF-based approaches and demonstrate the effectiveness of features learned from character-level and context words.

作者: Dehong Ma Sujian Li Houfeng Wang

作者单位: MOE Key Lab of Computational Linguistics,Peking University,Beijing 100871,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: 353-364

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）