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What and Who
Title:Knowledge-rich models for high-end NLP applications
Speaker:Prof. Simone Paolo Ponzetto
coming from:Universit├Ąt Mannheim
Speakers Bio:http://dws.informatik.uni-mannheim.de/en/people/professors/prof-dr-simone-paolo-ponzetto/
Event Type:Talk
Visibility:D5, MMCI
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Level:AG Audience
Language:English
Date, Time and Location
Date:Thursday, 13 February 2014
Time:11:00
Duration:60 Minutes
Location:Saarbr├╝cken
Building:E1 4
Please note: New Room!
Room:024
Abstract
The Web contains vast amounts of textual content which needs to be
automatically semantified (i.e. fully structured and annotated with
semantic information) in order to conform to the vision of a Web of
semantic data, and enable next-generation applications like, for
instance, semantic search. Semantic information, furthermore, is highly
intertwined with knowledge, since knowledge-rich methods have been shown
to achieve state-of-the-art performance on tasks that are essential for
generating semantic structure like word sense and entity disambiguation
and, conversely, semantified data can be used to further extend existing
repositories of machine-readable knowledge.

In this talk I will elaborate on this vision of a synergistic approach
to structured knowledge and semantic information by presenting an
overview of recent work on exploiting wide-coverage knowledge sources
for a variety of Natural Language Processing (NLP) tasks. We first
introduce methods to leverage multilingual knowledge from a very large
lexical database in order to achieve state-of-the-art performance on
different lexical understanding tasks, e.g., sense disambiguation and
word similarity. Next, we show how information from existing
wide-coverage ontologies, like YAGO or DBpedia, can be used to provide
structured (i.e., graph-based), semantically-rich representations of
texts, which can then be used to achieve robust performance on even more
complex tasks such as computing entity ranking and document similarity.

Our results confirm the notion that NLP applications can benefit
substantially from large amounts of knowledge to achieve human-level
performance on complex language processing tasks. Nevertheless, we argue
that much still remains to be done in terms of more sophisticated
modelling and depth of representation for both conceptual knowledge and
textual content.
Contact
Name(s):Petra Schaaf
Phone:5000
EMail:--email address not disclosed on the web
Video Broadcast
Video Broadcast:NoTo Location:
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Attachments, File(s):
  • Petra Schaaf, 02/10/2014 01:55 PM
  • Petra Schaaf, 02/10/2014 01:52 PM -- Created document.