24.917, Calls: Semantics, Computational Linguistics/France

Wed Feb 20 17:33:24 UTC 2013

LINGUIST List: Vol-24-917. Wed Feb 20 2013. ISSN: 1069 - 4875.

Subject: 24.917, Calls: Semantics, Computational Linguistics/France

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Date: Wed, 20 Feb 2013 12:32:32
From: Nabil Hathout [Nabil.Hathout at univ-tlse2.fr]
Subject: Current Issues in Distributional Semantics

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Full Title: Current Issues in Distributional Semantics 
Short Title: SemDis 2013 

Date: 21-Jun-2013 - 21-Jun-2013
Location: Sables d'Olonne, France 
Contact Person: Cécile Fabre
Meeting Email: cecile.fabre at univ-tlse2.fr
Web Site: http://www.taln2013.org/ateliers/appel-atelier-semantique-distributionnelle/ 

Linguistic Field(s): Computational Linguistics; Semantics 

Call Deadline: 29-Mar-2013 

Meeting Description:

SemDis 2013: Current Issues in Distributional Semantics
Workshop associated with the 20th TALN conference
June 21, 2013
Sables d’Olonne, France

In the course of the last two decades, significant progress has been made with regard to the automatic extraction of semantic knowledge from large-scale text corpora. Most work relies on Harris’ distributional hypothesis of meaning, which states that words that appear within the same contexts tend to be semantically related. This principle has inspired a substantial amount of research - mainly for English but also for other languages - and several survey articles have recently helped to consolidate the concepts and procedures used for distributional computations (Sahlgren, 2006; Turney and Pantel, 2010; Baroni and Lenci, 2010). In recent years, the distributional semantic approach has benefited from the availability of massive amounts of textual data and increased computational power, allowing for the application of these methods on a large scale. Still, a number of research topics remain open, with regard to the construction, the evaluation and the application of the semantic information that is induced by these methods.

Regarding the construction of distributional semantic resources, the nature of the corpus is a key issue, and its impact on the results requires further investigation. Today’s trend is to use massive corpora, moving away from Harris’ initial hypothesis which was based on the analysis of small, well-defined, and specialized corpora. A second important issue relates to the modeling of semantic compositionality within a distributional framework, such that not only individual words but also larger phrases can be taken into account (Mitchell et Lapata, 2008; Baroni & Zamparelli, 2010; Grefenstette & Sadrzadeh, 2011).

Relations between words tend to be very diverse. Regarding the evaluation of distributional models, we need a better understanding of the nature of semantic relations (synonymous, associative, analogous, etc.) induced by these models, and the impact of the distributional parameters on the induced relations (Sahlgren, 2006; Peirsman & Geeraerts, 2009). Secondly, large corpora generate resources so large that they are very difficult to explore and grasp. The manipulation of graphs within visualization systems suitable for their exploration can improve our knowledge on their content and structure.

Finally, distributional resources are useful for a large number of applications such as information retrieval, summarization, text segmentation, etc. Distributional features have been incorporated into a wide range of NLP tasks, such as named entity classification and paraphrasing (Kotlerman et al. 2010; Jonnalagadda et al. 2012). Linguists could equally benefit from these distributional approaches, as they provide a means to conduct large-scale studies of the semantic relations that may be discovered from large corpora.

Call for Papers:

We welcome papers that focus on any of the aforementioned topics, and in particular:

- The construction of distributional semantic resources 
- The nature of corpora within distributional semantics 
- Compositionality within a distributional framework 
- The use of distributional resources for linguistic analysis 
- The induction of specific semantic relations 
- The use of distributional methods within NLP tasks 
- Optimization techniques for distributional computations
- Visualization techniques for word spaces 

Important Dates:

Paper submission: March 29, 2013 
Acceptance notification: April 19, 2013 
Final version:  May 2, 2013 

Papers should be submitted in PDF format through EasyChair:

https://www.easychair.org/conferences/?conf=semdis2013 

Papers should be written in French or English, should count between 12 and 14 pages, and need to conform to the TALN style sheet, which is available on the conference website (http://www.taln2013.org/soumettre/). The selection criteria are those defined for the main conference.

Organizing Committee:

Cécile Fabre, CLLE, Toulouse, France 
Nabil Hathout, CLLE, Toulouse, France 
Philippe Muller, IRIT, Toulouse, France 
Tim Van de Cruys, IRIT, Toulouse, France 

Program Committee:

Stergos Afantenos, IRIT, Toulouse, France 
Yves Bestgen, UCL/CECL, Louvain-La-Neuve, Belgium 
Marie Candito, ALPAGE, Paris, France 
Eric de la Clergerie, ALPAGE, Paris, France 
Cécile Fabre, CLLE, Toulouse, France 
Olivier Ferret, CEA-LIST, Fontenay-aux-Roses, France 
Nabil Hathout, CLLE, Toulouse, France 
Philippe Muller, IRIT, Toulouse, France 
Adeline Nazarenko, LIPN, Paris, France 
Pascale Sébillot, IRISA, Rennes, France 
Ludovic Tanguy, CLLE, Toulouse, France 
Agnès Tutin, LIDILEM, Grenoble, France 
Tim Van de Cruys, IRIT, Toulouse, France 
Virginie Zampa, LIDILEM, Grenoble, France 

Contact: Cécile Fabre (cecile.fabre at univ-tlse2.fr) and Tim Van de Cruys (tim.vandecruys at irit.fr)

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