<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
<html>
<head>
<meta http-equiv="content-type" content="text/html; charset=ISO-8859-1">
</head>
<body bgcolor="#ffffff" text="#000000">
<small><font color="#3333ff">-------------------------- Final call
for papers --------------------------------<br>
</font></small><small><font color="#3333ff">PAPER SUBMISSION
DEADLINE: March 14, 2011<br>
<br>
<br>
</font></small><small><font color="#3333ff"> Joint Workshop
FAM-LbR/KRAQ'11<br>
<b>Learning by Reading and its Applications in Intelligent
Question-Answering</b><br>
with IJCAI 2011<br>
July 16-18, 2011<br>
<a class="moz-txt-link-freetext"
href="http://www.rutumulkar.com/FAM-LbR-KRAQ-2011.php">http://www.rutumulkar.com/FAM-LbR-KRAQ-2011.php</a><br>
<br>
Call for Papers<br>
-----------------<br>
<br>
It has been a long term dream of AI to develop systems that can
emulate human levels of language understanding and reasoning.
Recent foundational, methodological and technological
developments in Knowledge Representation (e.g. ontologies,
knowledge bases incorporating various forms of incompleteness or
uncertainty), Reasoning (e.g. data fusion-integration,
argumentation, decision theory, fuzzy logic, incomplete
knowledge bases, etc.), Natural Language Processing (such as
information extraction, relation detection) and formal
pragmatics (user models, intentions, etc.) make it possible to
foresee the elaboration of much more accurate, cooperative and
robust systems dedicated to understanding, learning and
answering questions from textual data, operating either on open
or closed domains. The time is right to start placing the pieces
from all these different areas to develop a unified system for
Learning by Reading and Automated Question Answering.<br>
<br>
Until now, most approaches for QA and Learning by Reading have
been either “narrow and deep” or “broad and shallow”. Many text
mining systems embody the latter. An important question arises
whether a “broad and deep” approach is a possibility at this
stage.<br>
<br>
The goal of this workshop is to bring together researchers from
different backgrounds (AI, NLP, linguistics, HLT and pragmatics)
to explore possibilities of integrating the different techniques
for building a system for Learning by Reading and/or Automated
Question Answering. The workshop will be focused on models for
intelligently analyzing data and cooperatively responding to the
user queries. This includes areas such as AI models for
processing data coming e.g. from search engines and models that
provide users with explanations and arguments about response
contents and the way they have been elaborated. Numerous
interesting questions arise, including, how can we evaluate such
systems automatically or semi-automatically? Is it possible to
run such systems on a massive scale? What role does commonsense
play in reasoning of textual data? Is it possible to extract
this commonsense knowledge automatically?<br>
<br>
Topics of interest include (but are not limited to)<br>
<br>
* Language processing:<br>
o Analysis of existing language resources such as
Wikipedia<br>
o Language analysis (such as question processing,
answer identification)<br>
o Language generation and Explanation production <br>
* Reasoning aspects:<br>
o Abductive/deductive, commonsense, and other
reasoning<br>
o Reasoning under uncertainty or with incomplete
knowledge, models for explanation production and argumentation<br>
o Information fusion-integration,<br>
o Knowledge extraction from text vs. using pre-built
knowledge resources<br>
o Bridging knowledge gaps in text through inference<br>
o Knowledge Integration into evolving models<br>
o Bootstrapping Learning <br>
* Pragmatic dimensions of intelligently answering questions:<br>
o User intentions, plans and goals recognition and
production<br>
o Conversational implicatures in responses, principles
for the design of cooperative systems.<br>
o Learning temporal sequences, causality, and other
semantics from text<br>
o Ontology learning, population, or expansion <br>
* Applications:<br>
o Question answering of semi-structured documents such
as wikipedia<br>
o Multimedia question answering, where you question a
more or less formal representation of the media objects<br>
o Spoken question answering (increasing uncertainty
caused by the speech recognition) <br>
* Evaluation:<br>
o Automatic Evaluation of learned Knowledge<br>
o Intrinsic evaluation of inference methods<br>
o Data-intensive vs Knowledge-intensive methods<br>
o Portability techniques for closed domains. <br>
<br>
Submission Information<br>
-----------------<br>
<br>
We welcome short papers (max 4 pages), describing projects or
ongoing research and long papers (max. 6 pages), that relate
more established results. Papers must be sent in .pdf format.
The following information MUST be included:<br>
<br>
* Title<br>
* Authors' names, affiliations, and email addresses<br>
* Topic(s) of the above list, as appropriate<br>
* Abstract (short summary up to 5 lines) <br>
<br>
Important Dates<br>
-----------------<br>
<br>
March 14, 2011 - Paper Submission<br>
April 25, 2011 - Acceptance Notification<br>
May 16, 2011 - Camera ready paper due<br>
Location<br>
<br>
FAM-LbR/KRAQ 2011 is held with IJCAI 2011 (July 16-18, 2011) in
downtown Barcelona. Local information can be found from the
conference website. Please use the IJCAI style sheets to prepare
your submission. Reviewing will not be blind. <br>
<br>
Organizing Co-Chairs<br>
-----------------<br>
<br>
Rutu Mulkar-Mehta (<a class="moz-txt-link-abbreviated"
href="mailto:me@rutumulkar.com">me@rutumulkar.com</a>),
Patrick Saint-Dizier (<a class="moz-txt-link-abbreviated"
href="mailto:stdizier@irit.fr">stdizier@irit.fr</a>)<br>
Eduard Hovy (<a class="moz-txt-link-abbreviated"
href="mailto:hovy@isi.edu">hovy@isi.edu</a>), Marie-Francine
Moens (<a class="moz-txt-link-abbreviated"
href="mailto:Sien.Moens@cs.kuleuven.be">Sien.Moens@cs.kuleuven.be</a>)<br>
Bernardo Magnini (<a class="moz-txt-link-abbreviated"
href="mailto:magnini@fbk.eu">magnini@fbk.eu</a>)<br>
Chris Welty (<a class="moz-txt-link-abbreviated"
href="mailto:welty@us.ibm.com">welty@us.ibm.com</a>)</font></small>
</body>
</html>