Natural language research announcements

["Roth, Dan" <danr AT illinois.edu>]

Please note that this semester the AIIS seminar will carry with it credit; 
register to "CS 591 Advanced Seminar, section NLP".

        Dan


-----Original Message-----
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[mailto:cogcomp-bounces AT cs.uiuc.edu]
 On Behalf Of Yonatan Bisk
Sent: Monday, January 17, 2011 2:22 PM
To: 
nl-uiuc AT cs.uiuc.edu;
 
aivr AT cs.uiuc.edu;
 
dais AT cs.uiuc.edu;
 
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 Girju, Corina R; Amir, Eyal
Subject: [cogcomp] AIIS talk: Jonathan Berant ( Jan 21 )

-- please _email_ me ( 
bisk1 AT illinois.edu
 ) your availability if you are interested in a meeting --

When:       Friday January 21 @ 2pm

Where:      3405 SC

Speaker:   Jonathan Berant  ( http://www.cs.tau.ac.il/~jonatha6/ )

Title:          Global Learning of Entailment Graphs

Abstract:   One of the key challenges in developing natural language 
understanding applications such as Question Answering, Information Retrieval, 
or Information Extraction is overcoming the variability of semantic 
expression, namely the fact that the same meaning can be expressed in natural 
language by many phrases. In this work, we address a crucial component of 
this problem: learning inference rules or entailment rules between natural 
language predicates, such as "X buy from Y --> Y sell to X".

Previous work has focused on estimating each entailment rule independently of 
others, but clearly there are interactions between different entailment 
rules. We address this issue by modelling the problem of learning entailment 
rules as a graph learning problem (termed "entailment graphs"), and attempt 
to learn graphs that are "coherent" in the sense that they obey certain 
global properties. We formulate the problem as an Integer Linear Program 
(ILP) and introduce two algorithms that scale the use of ILP solvers to 
larger entailment graphs. We learn entailment graphs in 2 scenarios: (1) 
where one of the arguments is instantiated (X increase asthma symptoms --> X 
affects asthma) (2) where the arguments are typed (Xcountry conquer Ycity 
-->Xcountry invade Ycity) and show an improvement in performance over 
previous state-of-the-art algorithms. We also show that our scaling 
techniques increase the recall of the algorithm without harming precision.

This work is based on the paper "Global Learning of Focused Entailment 
Graphs":
http://www.cs.tau.ac.il/~jonatha6/homepage_files/publications/ACL10.pdf
and on recently-submitted work performed at The University of Washington. 
This is joint work with Ido Dagan and Jacob Goldberger


Bio:        Jonathan Berant is a PhD student at Tel-Aviv University, working 
in Bar-Ilan University's NLP group under the supervision of Ido dagan and 
Jacob Goldberger

- Yonatan -

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