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<p class=MsoNormal><span style='color:black'>
Journal of Natural Language Engineering<o:p></o:p></span></p>
<p class=MsoNormal><span style='color:black'>
Special Issue on Textual Entailment<o:p></o:p></span></p>
<p class=MsoNormal><span style='color:black'><o:p> </o:p></span></p>
<p class=MsoNormal><span style='color:black'>
Second Call For Papers<o:p></o:p></span></p>
<p class=MsoNormal><span style='color:black'><o:p> </o:p></span></p>
<p class=MsoNormal style='text-indent:.5in'><span style='color:black'>Submissions
due by November 15, 2007<o:p></o:p></span></p>
<p class=MsoNormal><span style='color:black'><o:p> </o:p></span></p>
<p class=MsoNormal><span style='color:black'><o:p> </o:p></span></p>
<p class=MsoNormal><span style='color:black'>The goal of identifying textual
entailment - whether one piece of text can be plausibly inferred from another -
has emerged in recent years as a generic core problem in Natural</span><span
style='color:#1F497D'> </span><span style='color:black'>Language Understanding.
For instance, in order to answer the question "Who killed Kennedy?", </span>a
QA system may need to recognize that "Oswald killed Kennedy" can be
inferred from "the assassination of Kennedy by Oswald".<o:p></o:p></p>
<p class=MsoNormal><span style='color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoNormal>Work in this area has been largely driven by the PASCAL
Recognizing<span style='color:#1F497D'> </span>Textual Entailment (RTE)
challenges, a series of annual competitive<span style='color:#1F497D'> </span>meetings
(http://www.pascal-<span style='color:#1F497D'>n</span>etwork.org/Challenges/RTE3).
This work<span style='color:#1F497D'> </span>exhibits strong ties to some
earlier lines of research, particularly<span style='color:#1F497D'> </span>automatic
acquisition of paraphrases and lexical semantic<span style='color:#1F497D'> </span>relationships,
and unsupervised inference in applications such as<span style='color:#1F497D'> </span>question
answering, information extraction and summarization. It<span style='color:#1F497D'>
</span>has also opened the way to newer lines of <span style='color:#1F497D'>r</span>esearch
on more involved<span style='color:#1F497D'> </span>inference methods, on
knowledge representations needed to support<span style='color:#1F497D'> </span>this
natural language understanding challenge and on the use of<span
style='color:#1F497D'> </span>learning<span style='color:#1F497D'> m</span>ethods
in this context. RTE has fostered an active and<span style='color:#1F497D'> </span>growing
community of researchers focused on the problem of applied<span
style='color:#1F497D'> </span>entailment. The special issue of JNLE will
provide<span style='color:#1F497D'> a</span>n opportunity to<span
style='color:#1F497D'> </span>showcase some of the most important work in this
emerging area.<o:p></o:p></p>
<p class=MsoNormal><o:p> </o:p></p>
<p class=MsoNormal>Articles for this special issue are invited on all aspects
of textual entailment, aiming at a broader scope than exhibited within the RTE challenges.
Topics include, but are not limited to:<o:p></o:p></p>
<p class=MsoNormal><o:p> </o:p></p>
<p class=MsoNormal>* Representation levels, such as - Lexical, n-gram,
and substring overlap<span style='color:#1F497D'> </span><o:p></o:p></p>
<p class=MsoNormal> - Linguistic annotations (POS tags, syntactic
structure,<span style='color:#1F497D'> </span>semantic dependencies)<o:p></o:p></p>
<p class=MsoNormal>* Utilizing background knowledge, e.g. inference rules,
paraphrase<span style='color:#1F497D'> </span>templates, lexical relations<o:p></o:p></p>
<p class=MsoNormal>* Knowledge acquisition methods<o:p></o:p></p>
<p class=MsoNormal> - From corpora/Web, including acquiring
entailment/paraphrasing<span style='color:#1F497D'> </span>corpora<o:p></o:p></p>
<p class=MsoNormal> - From semantic resources like FrameNet, PropBank,
VerbNet, NOMLEX/NOMBANK<o:p></o:p></p>
<p class=MsoNormal>* Inference mechanisms, such as<o:p></o:p></p>
<p class=MsoNormal> - Similarity/subsumption metrics<o:p></o:p></p>
<p class=MsoNormal> - Tree-based distances and transformations<o:p></o:p></p>
<p class=MsoNormal> - Machine learning<o:p></o:p></p>
<p class=MsoNormal> - Logical inference using theorem provers<o:p></o:p></p>
<p class=MsoNormal>* The impact of entailment capabilities on applications<o:p></o:p></p>
<p class=MsoNormal>* Evaluation methods<o:p></o:p></p>
<p class=MsoNormal>* Data analysis<o:p></o:p></p>
<p class=MsoNormal><o:p> </o:p></p>
<p class=MsoNormal>Submission information:<o:p></o:p></p>
<p class=MsoNormal>Please consult the journal web site for instructions for
contributors (uk.cambridge.org/journals/nle/). Submissions should be sent by
email to JNLE_TE@cs.uiuc.edu (instead of the email address mentioned in the instructions
file). The message subject line should be “JNLE TE submission: <last
name of first author>”. <o:p></o:p></p>
<p class=MsoNormal><span style='color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoNormal>Submissions are due by November 15, 2007.<o:p></o:p></p>
<p class=MsoNormal><o:p> </o:p></p>
<p class=MsoNormal>Guest Editors:<o:p></o:p></p>
<p class=MsoNormal>Ido Dagan
(Bar Ilan University, Israel)<o:p></o:p></p>
<p class=MsoNormal>Bill Dolan
(Microsoft Research, USA)<o:p></o:p></p>
<p class=MsoNormal>Bernardo Magnini (FBK-irst, Italy)<o:p></o:p></p>
<p class=MsoNormal>Dan
Roth (UIUC, USA)<o:p></o:p></p>
<p class=MsoNormal><o:p> </o:p></p>
<p class=MsoNormal><o:p> </o:p></p>
<p class=MsoNormal><o:p> </o:p></p>
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