[Corpora-List] CFP SI on Modality and Negation - Computational Linguistics Journal

Thu Jan 20 09:20:29 UTC 2011

2nd CALL FOR PAPERS

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A Special Issue of the Computational Linguistics Journal
on Modality and Negation

http://cljournal.org/specials/modality-and-polarity.html

Submission deadline: 10 March 2011
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Computational linguistics has seen achievements in handling language at 
different levels of linguistic abstraction, from tokenization to semantic 
role labeling. Given a sentence, systems can more or less reliably 
determine who does what to whom when and where. However, texts do not 
always express factual information; on the contrary, language is often 
used to express uncertainty, opinion, evaluation, or doubt. Accordingly, 
computational linguistics has started to take into account the subjective 
aspects of language. There is now research that focuses also on 
determining who states that someone does something somewhere at a certain 
point in time (perspective) and based on what evidence (evidentiality), 
how certain someone is about stating something (certainty), the truth 
value of the facts being stated (negation), or the subjective evaluation 
of these facts (positive/negative opinion).

Linguistic phenomena such as modality and negation allow the expression of 
subjective aspects of meaning. Modality and negation are two 
information-level concepts that are well described from a philosophical 
perspective. Modality (Palmer 1986) is related to the attitude of the 
speaker towards her statements in terms of degree of certainty, 
reliability, subjectivity, sources of information, and perspective. It is 
related to other concepts like hedging (Hyland 1998), evidentiality 
(Aikhenvald 2004), uncertainty (Rubin et al. 2005), and factuality (Saurí 
2008). Negation (Tottie 1991, Horn 2001) is used to position information 
as a counterfact, a fact that does not hold in the world. Both modality 
and negation are complex linguistic phenomena that are challenging both 
from a theoretical and a computational point of view. Their complexity is 
due to the fact that both phenomena interact with each other (de Haan 
1997) and with other aspects of the linguistic context, such as mood, 
tense, and lexis. While modality and negation tend to be lexically marked, 
the class of markers is relatively large and heterogeneous. For example, 
while negation words such as "not" are clear indicators of negation, other 
terms such as modals, adverbs, conjunctions and multi-word expressions can 
also express negation and subjectivity. Moreover, processing modality and 
negation involves disambiguating the markers and determining their scope.

The treatment of modality and negation is very relevant for all NLP 
applications that involve deep text understanding. This includes 
applications that need to discriminate between factual and non-factual 
information (uncertain facts, opinions, attitudes, emotions, and beliefs), 
such as information extraction, opinion mining, sentiment analysis, text 
mining, and question answering, as well as other applications that process 
the meaning of texts, such as recognizing textual entailment, 
paraphrasing, and summarization. Hence, the adequate modeling of these 
phenomena is of crucial importance to the NLP community as a whole. While 
the area is still relatively new compared to areas like machine 
translation, parsing or semantic role labeling, it is now growing quickly.

TOPICS

For this special issue we solicit full-length article submissions 
describing innovative and challenging research on aspects of the 
computational modelling and processing of modality and negation. We 
specifically invite submissions that take into account linguistic aspects 
of the phenomena and bring a theoretical basis to research on computing 
the factuality and certainty of the events in a statement, finding the 
source and evidence for the statement of a fact, and determining whether a 
statement has a truth value. We encourage submissions that have a 
substantial analysis component, in the form of an analysis of the task and 
data and/or an error analysis of the proposed method. Submissions can 
address aspects of either modality or negation or both, provided that they 
lead to an enhanced understanding of the phenomena, as opposed to a 
straightforward engineering solution.

Possible topics include, but are not limited to:

- Linguistically informed modelling of modality and negation for NLP
- Analysis of the relevant information/knowledge involved in processing
   modality and negation
- The computational complexity of processing modality and negation
- Novel machine learning approaches for learning modality and negation
- Processing modality and negation across domains and genres
- The interaction of modality and negation for determining the factuality
   of events
- The influence of the linguistic context on the processing of modality
   and negation
- Evaluation of systems: metrics and application-based evaluation

IMPORTANT DATES

Submission of full articles: 10 March 2011
Preliminary decisions to authors:  31 June 2011
Submission of revised articles: 30 August 2011
Final decisions to authors: 18 October 2011
Final versions due from authors: 1 November 2011

SUBMISSION INSTRUCTIONS

Articles submitted to this special issue must adhere to the Style 
Guidelines of the Computational Linguistics Journal 
(http://cljournal.org/style.html).  The submission guidelines can be found 
in the  Computational Linguistics web site 
(http://cljournal.org/submissions.html). As in regular submissions to the 
journal, paper submissions should be made through the CL electronic 
submission system (http://cljournal.org/submissions/index.php/cljournal).

GUEST EDITORS

Roser Morante

    CLiPS - University of Antwerp, Belgium
    roser.morante at ua.ac.be

Caroline Sporleder

    Computational Linguistics and Phonetics - Saarland University, Germany
    csporled at coli.uni-sb.de

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Caroline Sporleder
Cluster of Excellence MMCI / Computational Linguistics
Saarland University
csporled at coli.uni-sb.de
http://www.coli.uni-saarland.de/~csporled
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