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What and Who
Title:Adventures in Representation Learning Land for Natural Language Processing
Speaker:Prof. Tim Baldwin
coming from:University of Melbourne
Speakers Bio:http://people.eng.unimelb.edu.au/tbaldwin/

Tim Baldwin is a Professor in the Department of Computing and Information
Systems, The University of Melbourne, and an Australian Research Council
Future Fellow. He has previously held visiting positions at Cambridge
University, University of Washington, University of Tokyo, Saarland
University, NTT Communication Science Laboratories, and National Institute of
Informatics. His research interests include text mining of social media,
computational lexical semantics, information extraction and web mining, with a
particular interest in the interface between computational and theoretical
linguistics. Current projects include web user forum mining, monitoring and
text mining of Twitter, and text analytics for the creative industries.

Tim completed a BSc(CS/Maths) and BA(Linguistics/Japanese) at The University
of Melbourne in 1995, and an MEng(CS) and PhD(CS) at the Tokyo Institute of
Technology in 1998 and 2001, respectively. Prior to joining The University of
Melbourne in 2004, he was a Senior Research Engineer at the Center for the
Study of Language and Information, Stanford University (2001-2004).

Event Type:MPI-Kolloquium
Visibility:D1, D2, D3, D4, D5, SWS, RG1, MMCI
We use this to send out email in the morning.
Level:Public Audience
Language:English
Date, Time and Location
Date:Monday, 1 August 2016
Time:13:45
Duration:75 Minutes
Location:Saarbr├╝cken
Building:E1 4
Room:024
Abstract
In this talk, I will present recent work on representation learning for NLP,
focusing on the question of what it buys us in terms of improved "robustness".
I will explore this in three contexts: (1) open-class lexical relation
classification, where we explore the general utility of vector differences
over word embeddings to capture the relation between ordered word pairs; (2)
sequence tagging (POS tagging, chunk parsing, named entity recognition and
multiword expression identification) under varying conditions of
lexical/domain (mis)match; and (3) cross-domain named entity recognition under
varying conditions of class label mismatch.
Contact
Name(s):Daniela Alessi
Phone:5000
EMail:--email address not disclosed on the web
Video Broadcast
Video Broadcast:NoTo Location:
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Created by:Daniela Alessi/MPI-INF, 07/28/2016 01:59 PMLast modified by:Uwe Brahm/MPII/DE, 11/24/2016 04:13 PM
  • Daniela Alessi, 07/28/2016 02:06 PM -- Created document.