时间：5月27日 14：00-16：00
地点：校本部理工楼504
讲座一：
Title: Joint Event Extraction via Structured Prediction with Global Features
Abstract: Traditional approaches to the task of automatic event extraction
usually rely on sequential pipelines with multiple stages, which suffer from
error propagation since event triggers and arguments are predicted in
isolation by independent local classifiers. By contrast, we propose a joint
framework based on structured prediction which extracts triggers and
arguments together so that the local predictions can be mutually improved.
In addition, we propose to incorporate global features which explicitly
capture the dependencies of multiple triggers and arguments. Experimental
results show that our joint approach with local features outperforms the
pipelined baseline, and adding global features further improves the
performance significantly. Our approach advances state-of-the-art
sentence-level event extraction, and even outperforms previous argument
labeling methods which use external knowledge from other sentences and
documents.
Bio: Heng Ji is an associate professor in Departments of Computer Science
and Linguistics at City University of New York. She received her Ph.D. in
Computer Science from New York University in 2007. Her research interests
focus on Natural Language Processing, especially on Cross-source Information
Extraction (IE) and Knowledge Base Population (KBP). She received US NSF
CAREER award in 2010 and AI"s top 10 to Watch award in 2013. She served as
the coordinator of the NIST TAC KBP task in 2010 and 2011, and the IE area
chair of NAACL-HLT2012 and ACL2013.
讲座二：
Title: Abstract Meaning Representation for Semantics-Banking
Abstract: We describe Abstract Meaning Representation (AMR), a semantic
representation language in which we are writing down the meanings of
thousands of English sentences. We hope that a semantics bank of simple,
whole-sentence semantic structures will spur new work in statistical natural
language understanding and generation, like the Penn Treebank encouraged
work on statistical parsing. This talk gives an overview of AMR and tools
associated with it.
Bio: Kevin Knight is a Senior Research Scientist and Fellow at the
Information Sciences Institute of the University of Southern California
(USC), and a Research Professor in USC"s Computer Science Department. He
received a PhD in computer science from Carnegie Mellon University and a
bachelor"s degree from Harvard University. Professor Knight"s research
interests include natural language processing, machine translation, automata
theory, and decipherment. In 2001, he co-founded Language Weaver, Inc.,
which provides commercial machine translation solutions, and in 2011, he
served as President of the Association for Computational Linguistics.