Journal of Natural Language Engineering (JNLE)

Special Issue on Textual Entailment

Call For Papers

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 Language Understanding. For instance, in order to answer the question 'Who killed Kennedy?', a QA system may need to recognize that 'Oswald killed Kennedy' can be inferred from 'the assassination of Kennedy by Oswald'. Work in this area has been largely driven by the PASCAL Recognizing Textual Entailment (RTE) challenges, a series of annual competitive meetings (http://www.pascal-network.org/Challenges/RTE3). This work exhibits strong ties to some earlier lines of research, particularly automatic acquisition of paraphrases and lexical semantic relationships, and unsupervised inference in applications such as question answering, information extraction and summarization. It has also opened the way to newer lines of research on more involved inference methods, on knowledge representations needed to support this natural language understanding challenge and on the use of learning methods in this context. RTE has fostered an active and growing community of researchers focused on the problem of applied entailment.

The special issue of JNLE will provide an opportunity to showcase some of the most important work in this emerging area. 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: *Representation levels, such as -Lexical, n-gram, and substring overlap -Linguistic annotations (POS tags, syntactic structure, semantic dependencies) *Utilizing background knowledge, e.g. inference rules, paraphrase templates, lexical relations *Knowledge acquisition methods - From corpora/Web, including acquiring entailment/paraphrasing corpora - From semantic resources like FrameNet, PropBank, VerbNet, NOMLEX/NOMBANK *Inference mechanisms, such as - Similarity/subsumption metrics - Tree-based distances and transformations - Machine learning - Logical inference using theorem provers

*The impact of entailment capabilities on applications *Evaluation methods *Data analysis Submission information: Please consult the journal web site for instructions for contributors (http://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'.

Submissions are due by November 15, 2007. Guest Editors: Ido Dagan (Bar Ilan University, Israel) Bill Dolan (Microsoft Research, USA) Bernardo Magnini (FBK-irst, Italy) Dan Roth (UIUC, USA)