Aspect-Oriented Syntax Network for Aspect-Based Sentiment Analysis，中山大学数据科学与计算机学院权小军教授，第八届全国社会媒体处理大会SMP2019

报告主题：Aspect-Oriented Syntax Network for Aspect-Based Sentiment Analysis

报告摘要：Aspect-based sentiment analysis aims to determine the sentimental polarity towards a specific aspect in reviews or comments. Recent attempts mostly adopt attention-based mechanisms to link opinion words to their respective aspects in an implicit way. However, due to the tangle of multiple aspects or opinion words occurred in one sentence, the models often mix up the linkages. In this paper, we propose to encode sentence syntax explicitly to improve the effect of the linkages. We define an aspect-oriented dependency tree structure, which is reshaped and pruned from an ordinary parse tree, to express useful syntax information. The new tree is then encoded into a multifaceted syntax network, to be used in combination with attention-based models for prediction. Experimental results on three datasets from SemEval 2014 and Twitter show that, with our syntax network, the aspect-sentiment linkages can be better established and the attention-based models are substantially improved as a result.

嘉宾简介：权小军，教授，博士生导师。先后于中国科学技术大学计算机系、香港城市大学计算机系、美国罗格斯大学商学院、美国普渡大学计算机系、香港城市大学语言学与翻译系、新加坡科技研究局资讯通信研究院从事自然语言处理、文本挖掘和机器学习的研究工作，在国际知名期刊和会议如IEEE T-PAMI，ACM TOIS，ACL，IJCAI，SIGIR等发表论文30余篇。权小军2012年毕业于香港城市大学，获博士学位，回国前就职于新加坡科技研究局资讯通信研究院，任研究科学家，期间除从事相关方向的基础研究外，也同工业界紧密合作探索研究成果的应用。