Paper: Semantic Role Labeling for News Tweets

ACL ID C10-1079
Title Semantic Role Labeling for News Tweets
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2010

News tweets that report what is happen- ing have become an important real-time information source. We raise the prob- lem 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 spe- cific SRL system to resolve the problem. A large volume of training data is auto- matically labeled, by leveraging the ex- isting 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.