Dependency Parsing for Weibo: An Efficient Probabilistic Logic Programming Approach

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-shelf Stanford Chinese parser, as well as a strong MaltParser baseline that is trained on the same in-domain data.