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In this work, we try to understand the differences between exact and approximate inference algorithms in structured prediction. We compare the estimation and approximation error of both underestimate and overestimate models. The result shows that, from the perspective of learning errors, performances of approximate inference could be as good as exact inference. The error analyses also suggest a new margin for existing learning algorithms. Empirical evaluations on text classification, sequential labelling and dependency parsing witness the success of approximate inference and the benefit of the proposed margin.
Author Information
Yuanbin Wu (East China Normal University)
Man Lan
Shiliang Sun (East China Normal University)
Qi Zhang (Fudan University)
Xuanjing Huang (Fudan University)
Xuanjing Huang is a Professor of the School of Computer Science, Fudan University, Shanghai, China. She received her PhD degree in Computer Science from Fudan University in 1998. From 2008 to 2009, she is a visiting scholar in CIIR, UMass Amherst. Her research interest includes text retrieval, natural language processing, and data intensive computing. She has published dozens of papers in several major conferences including SIGIR, ACL, ICML, IJCAI, AAAI, CIKM, ISWC, EMNLP, WSDM and COLING. She has also translated the second version of “Modern Information Retrieval” to Chinese. In the research community, she served as the organizer of WSDM 2015, competition chair of CIKM 2014, tutorial chair of COLING 2010, SPC or PC member of past WSDM, SIGIR, WWW, CIKM, ACL, IJCAI, EMNLP and many other conferences.
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