Lecture Series in Pattern Recognition
题 目（TITLE）：Parsing with an explicit semantic model
讲 座 人（SPEAKER）：Prof. Junichi Tsujii (Principal Researcher, Microsoft Research Asia)
主 持 人 (CHAIR)：Prof. Chengqing Zong
时 间 (TIME)：15:50PM, May 9 (Wednesday), 2012
地 点 (VENUE)：1115 Meeting Room
Although statistical modeling of language has made significant progress, parsing and semantic interpretation of a sentence still remain major challenges in NLP. Careful examination of parsing results reveals that the accuracy of semantically crucial problems such as PP-attachment, identification of antecedents of relative clauses, scope determination of coordinated phrases still remain less than 80%. On the other hand, recently mining technologies have provide NLP with much richer semantic/knowledge resources. In this talk, I will talk about our recent research on parsing with an explicit semantic model.
Junichi Tsujii is Principal Researcher of Microsoft Research Asia (MSRA). Before moving to MSRA (May, 2011), he was Professor of Natural Language Processing in the Department of Computer Science, University of Tokyo and Professor of Text Mining in School of Computer Science, University of Manchester, U.K. . He remains to be scientific advisor of the UK National Centre for Text Mining (NaCTeM) as well as visiting professor of University of Manchester. He has worked since 1973 in Natural Language Processing, Question Answering, Text Mining and Machine Translation. He gave keynote speeches and invited talks at many conferences such as Coling (1986), ACL (1991), ACL (2000), LREC (2004), IWSL (2004), SMBM (2005), ICSB (2006), BioCreative(2007), IEEE-ASRU(2007), BioCreative III (2010), Cicling (2011), NIH workshop (2012) etc. He was President of ACL (Association for Computational Linguistics, 2006) and President of IAMT (International Association for Machine Translation (2002-2004). He is Permanent member of ICCL (International Committee for Computational Linguistics, 1992-).
His recent research achievements include (1) Deep semantic parsing based on feature forest model, (2) Efficient search algorithms for statistical parsing, (3) Improvement of estimator for maximum entropy model, and (4) Construction of the gold standard corpus (GENIA) for Bio Text Mining and application of NLP techniques to text mining in the biomedical domain.