Time: 1245pm-145pm, Friday, Feb 19 Place: Room 6496, CUNY Graduate Center, 365 Fifth Ave (34str&35str). Speaker: Zheng Chen (CUNY) Title: Can One Language Bootstrap the Other: A Case Study on Event Extraction Abstract: We propose a new bootstrapping framework using cross-lingual information projection. We demonstrate that this framework is particularly effective for a challenging NLP task which is situated at the end of a pipeline and thus suffers from the errors propagated from up-stream processing and has low performance baseline. Using Chinese event extraction as a case study and bitexts as a new source of information, we present three bootstrapping techniques. We first conclude that the standard mono-lingual bootstrapping approach is not so effective. Then we exploit a second approach that potentially benefits from the extra information captured by an English event extraction system and projected into Chinese. Such a cross-lingual scheme produces significant performance gain. Finally we show that the combination of mono-lingual and cross-lingual information in bootstrapping can further enhance the performance. Ultimately this new framework obtained 10.1% relative improvement in trigger labeling (F-measure) and 9.5% relative improvement in argument-labeling. Speaker Bio: Zheng Chen is a Ph.D. student in Computer Science at the Graduate Center, the City University of New York. He is a member of BLENDER lab directed by Prof. Heng Ji. His research interests generally lie in computational linguistics and statistical machine learning, especially, cross-document cross-lingual information extraction, i.e., how to identify important facts (entities, relations, events) from web-scale corpus, how to resolve multiple mentions of the same entity/event. He has published 6 papers at NLP conferences, such as NAACL, ACL.