We are pleased to announce our first CUNY-NLP seminar of the Spring
semester. Please note that we have a new room and a new time for the
seminar this semester. If you are interested in meeting with the
speaker, please contact Prof. Matt Huenerfauth <ma...@cs.qc.cuny.edu>.
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
We propose a new bootstrapping framework using cross-lingual
projection. We demonstrate that this framework is particularly
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
bootstrapping techniques. We first conclude that the standard
mono-lingual bootstrapping approach is not so effective. Then we
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.
Zheng Chen is a Ph.D. student in Computer Science at the Graduate
the City University of New York. He is a member of BLENDER lab
by Prof. Heng Ji. His research interests generally lie in
linguistics and statistical machine learning, especially, cross-
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.
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