学术报告:Slot Filling Validation by Truth Finding

时间: 5月27日(周二)上午 9:30

地点: 计算机研究所一楼106报告厅

报告人:Prof. Heng Ji (Rensselaer Polytechnic Institute)

报告题目: Slot Filling Validation by Truth Finding

摘要:Information Extraction using multiple information sources and systems is beneficial due to multisource/ system consolidation and challenging due to the resulting inconsistency and redundancy.

We integrate IE and truth-finding research and present a novel unsupervised multi-dimensional truth finding framework which incorporates signals from multiple sources, multiple systems and multiple pieces of evidence by knowledge graph construction through multi-layer deep linguistic analysis. Experiments on the case study of Slot Filling Validation demonstrate that our approach can find truths accurately (9.4% higher F-score than supervised methods) and efficiently (finding 90% truths with only one half the cost of a baseline without credibility estimation).

报告人简介:Heng Ji is Edward P. Hamilton Development Chair Associate Professor in Computer Science Department of Rensselaer Polytechnic Institute. She received her B.A. and M.A. in Computational Linguistics from Tsinghua University in 2000 and 2002 respectively; and her M.S. and Ph.D. in Computer Science from New York University in 2005 and 2007 respectively. Her research interests include Natural Language Processing, Data Mining, Information Networks and Social Networks, and Security. She received Google Research Awards in 2009 and 2014, NSF CAREER award in 2009, Sloan Junior Faculty Award and IBM Watson Faculty Award in 2012, PACLIC2012 Best Paper Runner-up, "Best of SDM2013" paper, "Best of ICDM2013" paper and "AI's 10 to Watch" Award by IEEE Intelligent Systems in 2013. She coordinated the NIST TAC Knowledge Base Population task in 2010, 2011 and 2014, served as the vice Program Committee Chair for IEEE/WIC/ACM WI2013, the Information Extraction area chair for NAACL2012, ACL2013, EMNLP2013 and NLPCC2014, and the Local Co-chair for IJCAI2016. Her research is funded by the U.S. NSF, ARL, DARPA, Google and IBM.

 
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