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COLING
2010

Semantic Role Labeling for News Tweets

10 years 1 months ago
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.
Xiaohua Liu, Kuan Li, Bo Han, Ming Zhou, Long Jian
Added 13 May 2011
Updated 13 May 2011
Type Journal
Year 2010
Where COLING
Authors Xiaohua Liu, Kuan Li, Bo Han, Ming Zhou, Long Jiang, Zhongyang Xiong, Changning Huang
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