Date: 18-Mar-2009
From: Marco Pennacchiotti <marco.pennacchiotti 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
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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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