10.1184/R1/6475541.v1 William Yang Wang William Yang Wang Lingpeng Kong Lingpeng Kong Kathryn Mazaitis Kathryn Mazaitis William W. Cohen William W. Cohen Dependency Parsing for Weibo: An Efficient Probabilistic Logic Programming Approach Carnegie Mellon University 2014 Machine Learning 2014-10-01 00:00:00 Journal contribution https://kilthub.cmu.edu/articles/journal_contribution/Dependency_Parsing_for_Weibo_An_Efficient_Probabilistic_Logic_Programming_Approach/6475541 <p>Dependency parsing is a core task in NLP, and it is widely used by many applications such as information extraction, question answering, and machine translation. In the era of social media, a big challenge 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 treebank with more than 18K tokens from Sina Weibo (the Chinese equivalent of Twitter). We formulate the dependency parsing problem as many small and parallelizable arc prediction tasks: for each task, we use a programmable probabilistic firstorder 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.</p>