讲座题目：Neural Networks for Target-dependent Twitter Sentiment Classification

主讲人：张岳 助理教授

讲座时间：2016年10月25日（星期二）下午14:00-15:30

讲座地点：中原工学院创业科技园，6号楼5层，舆情中心指挥大厅

主讲人简介：新加坡技术与设计大学助理教授。2003年取得清华大学计算机科学与技术本科学位。2006年取得牛津大学计算机科学硕士学位。2010年取得牛津大学计算机科学博士学位。2010-2012期间在剑桥大学从事博士后研究工作。主要研究方向包括基于机器学习的自然语言处理、文本挖掘、文本分析等。现任国际期刊TACL编委，国际会议COLING14、NAACL15、EMNLP16及国内会议NLPCC14、NLPCC16、CNCCL16的领域主席。

   Yue Zhang is currently an assistant professor at Singapore University of Technology and Design. Before joining SUTD in July 2012, he worked as a postdoctoral research associate in University of Cambridge, UK. Yue Zhang received his DPhil and MSc degrees from University of Oxford, UK, and his BEng degree from Tsinghua University, China. His research interests include natural language processing, machine learning and artificial Intelligence. He has been working on statistical parsing, parsing, text synthesis, machine translation, sentiment analysis and stock market analysis intensively. Yue Zhang serves as the reviewer for top journals such as Computational Linguistics, Transaction of Association of Computational Linguistics and Journal of Artificial Intelligence Research. He is in the editorial board of TACL and TALLIP, being the associate editor for the latter. He is also PC member for conferences such as ACL, COLING, EMNLP, NAACL, EACL, AAAI and IJCAI. He was the area chairs of COLING 2014, NAACL 2015, EMNLP 2015, EMNLP 2016.

讲座内容提要：Targeted sentiment analysis, which has attracted increasing research attention, classifies the sentiment polarity towards each target entity mention in given text documents. Seminal methods extract manual discrete features from automatic syntactic parse trees in order to capture semantic information of the enclosing sentence with respect to a target entity mention. We claim that competitive accuracies can be achieved without using syntactic parsers, which can be highly inaccurate on noisy text such as tweets. This is achieved by applying pattern-based approach on distributed word representations with rich neural pooling functions over a simple and intuitive segmentation of tweets according to target entity mentions. Furthermore, we extend the idea by proposing a sentence-level neural model to address the limitation of pooling functions, which do not explicitly model tweet-level semantics. First, a bi-directional gated neural network is used to connect the words in a tweet so that pooling functions can be applied over the hidden layer instead of words for better representing the target and its contexts. Second, a three-way gated neural net- work structure is used to model the interaction between the target mention and its surrounding contexts. Experiments show that our proposed model gives significantly higher accuracies compared to the current best method for targeted sentiment analysis.

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计算机学院、研究生处

2016年10月21日