Paper: Dependency Parsing for Weibo: An Efficient Probabilistic Logic Programming Approach

ACL ID D14-1122
Title Dependency Parsing for Weibo: An Efficient Probabilistic Logic Programming Approach
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2014
Authors

Dependency parsing is a core task in NLP, and it is widely used by many applica- tions such as information extraction, ques- tion answering, and machine translation. In the era of social media, a big chal- lenge is that parsers trained on traditional newswire corpora typically suffer from the domain mismatch issue, and thus perform poorly on social media data. We present a new GFL/FUDG-annotated Chinese tree- bank with more than 18K tokens from Sina Weibo (the Chinese equivalent of Twit- ter). We formulate the dependency pars- ing problem as many small and paralleliz- able arc prediction tasks: for each task, we use a programmable probabilistic first- order logic to infer the dependency arc of a token in the sentence. In experiments, we show that the proposed model outperforms an off-the-s...