Semantic Role Labeling for News Tweets

News tweets that report what is happening have become an important real-time information source. We raise the problem of Semantic Role Labeling (SRL) for news tweets, which is meaningful for fine grained information extraction and retrieval. We present a self-supervised learning approach to train a domain specific SRL system to resolve the problem. A large volume of training data is automatically labeled, by leveraging the existing SRL system on news domain and content similarity between news and news tweets. On a human annotated test set, our system achieves state-of-the-art performance, outperforming the SRL system trained on news.

• Type: Journal
• Year: 2010
• Where: COLING
• Authors: Xiaohua Liu, Kuan Li, Bo Han, Ming Zhou, Long Jiang, Zhongyang Xiong, Changning Huang
• Keywords: COLING 2010, Computational Linguistics, Domain Specific Srl, Important Real-time Information, Semantic Role Labeling