20.945, Calls: Computational Ling/Natural Language Engineering (Jrnl)

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LINGUIST List: Vol-20-945. Wed Mar 18 2009. ISSN: 1068 - 4875.

Subject: 20.945, Calls: Computational Ling/Natural Language Engineering (Jrnl)

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1)
Date: 18-Mar-2009
From: Marco Pennacchiotti < marco.pennacchiotti at gmail.com >
Subject: Natural Language Engineering
 

	
-------------------------Message 1 ---------------------------------- 
Date: Wed, 18 Mar 2009 10:55:32
From: Marco Pennacchiotti [marco.pennacchiotti at gmail.com]
Subject: Natural Language Engineering

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Full Title: Natural Language Engineering 


Linguistic Field(s): Computational Linguistics;Text/Corpus Linguistics 

Call Deadline: 30-Jun-2009 

Call for Papers

Journal of Natural Language Engineering
Special Issue on: Distributional Lexical Semantics
       
URL: http://art.uniroma2.it/jnle
	   
*********************************************************

In the last decades, vector space models (VSM) have received a growing 
attention in different fields of Artificial Intelligence, ranging from natural 
language processing (NLP) and cognitive science, to vision analysis and 
applications in the humanities. The basic idea of VSM is to represent entities 
as vectors in a geometric space, so that their similarity can be measured 
according to distance metrics in the space.
VSM have demonstrated to successfully model and solve a variety of problems, 
such as metaphor detection and analysis, priming, discourse analysis, and 
information retrieval.

In computational linguistics, the Distributional Hypothesis  leverages the 
notion of VSM to model the semantics of words and other linguistic entities. 
The hypothesis was autonomously elaborated in different works, and has been 
since then applied through different settings. 
The hypothesis' core states that 'a word is defined by the company it keeps', 
i.e. by the set of linguistic contexts in which it appears. 

Despite the growing popularity of distributional approaches, existing 
literature raises issues on many important aspects that have still to be 
addressed. Examples are: the need of comparative in depth analyses of the 
semantic properties captured by different types of distributional models; the 
application of new geometrical approaches as the use of quantum logic
operators 
or tensor decomposition; the study of the interaction between 
distributional approaches and supervised machine learning, as the adoption of 
kernel methods based on distributional information; the application of 
distributional techniques in real world applications and in other fields.

Topics
======

The goal of the special issue is to offer a common journal venue where to 
gather and summarize the state of the art on distributional techniques applied 
to lexical semantics, as a cornerstone in computational linguistics research. 
As a side effect, the aim is also to propose a systematic and harmonized view 
of the works carried out independently by different researchers in the last 
years, which sometimes resulted in diverging and somehow inconsistent uses of 
terminology and axiomatizations. 
The special issue in particular focuses on the following areas of interest, 
building on topics proposed for the GEMS workshop (EACL 2009, Athens, 
http://art.uniroma2.it/gems):

     * Comparisons analysis of different distributional spaces
       (document-based, word-based, syntax based and others) and their
       parameters (dimension, corpus size, etc.)
     * Eigenvector methods (e.g. Singular Value and Tucker Decomposition)
     * Higher order tensors and Quantum Logic extensions
     * Feature engineering in machine learning models
     * Computational complexity and evaluation issues
     * Graph-based models over semantic spaces
     * Logic and inference in semantic spaces
     * Cognitive theories of semantic space models
     * Applications in the humanities and social sciences
     * Application of distributional approaches in :
           o Word sense disambiguation and discrimination
           o Selectional preference induction
           o Acquisition of lexicons and linguistic patterns
           o Conceptual clustering
           o Kernels methods for NLP (e.g. relation extraction and
             textual entailment)
           o Quantitative extensions of Formal Concept Analysis
           o Modeling of linguistic and ontological knowledge

Call deadline: 30-Jun-2009




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