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C A L L f o r P A P E R
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.
It follows that two target words appearing in similar contexts likely have
similar or related meanings ('distributional similarity'). Different types of
contexts (e.g. bag-of-words, syntactic relations, documents) tend to capture
different semantic relations between target words (e.g. relatedness,
similarity, topicality).
Practical uses of the distributional hypothesis are today very popular, in
various large scale linguistic learning tasks and applications, such as
harvesting thesauri, word sense disambiguation, inference rules harvesting,
selectional preference acquisition, conceptual clustering, modeling frame
semantics information, question answering and synonym detection.
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.
The special issue follows up most recent and similar efforts to summarize and
harmonize researches on distributional techniques. We here refer to the
'Contextual Information in Semantic Space Models' workshop (2007); the ESSLLI
workshop on 'Distributional Lexical Semantics' (2008); and the 'SigLex-SigSem
GEMS workshop' (2009). All these workshops indicate the growing interest in the
area in the last years.
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Topics
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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. A further goal is to increase awareness in the
computational linguistic community about cutting-edge studies on geometrical
models, machine learning applications and experiences in different scientific
fields.
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
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Important Dates
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Call for Paper : March 2009
Submissions deadline : 30 June 2009
First Evaluation Results : October 2009
Second Submission : January 2010
Final Acceptance : March 2010
Special Issue : May 2010
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Submission
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Articles submitted to this special issue must adhere to the Journal Style Guidelines.
Style Guide and LaTeX style files can be found at:

Articles are to be sent electronically by email in Adobe's PDF format. We encourage authors to keep their submissions below 30 pages. Authors should submit their papers electronically to
basili(at)info(dot)uniroma2(dot)it.
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Editorial Board
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- Guest Editors
Roberto Basili (University of Roma Tor Vergata, Italy)
Marco Pennacchiotti (Yahoo! Inc., Santa Clara, USA)
- Guest Editorial Board
Marco Baroni (University of Trento, Italy)
Michael W. Berry (University of Tenneesee)
Johan Bos (University of Roma "La Sapienza", Italy)
Paul Buitelaar (DFKI, Germany)
John A. Bullinaria (University of Birmingham, UK)
Rodolfo Dal Monte (University of Venice, Italy)
Susan Dumais (Microsoft Research)
Katrin Erk (University of Texas, US)
Stefan Evert (University of Osnabruck, Germany)
Gregory Grefenstette (Exalead S.A., France)
Alfio Massimiliano Gliozzo (STLab - ISTC - CNR, Italy )
Mirella Lapata (University of Edinburgh, UK)
Alessandro Lenci (University of Pisa, Italy)
Jussi Karlgren (Swedish Institute of Computer Science, Sweden)
Will Lowe (University of Nottingham, UK)
Diana McCarthy (University of Sussex)
Alessandro Moschitti (University of Trento, Italy)
Saif Mohammad (University of Mryland, US)
Sebastian Pado (Stanford University, US)
Patrick Pantel (Yahoo! Inc., US)
Ted Pedersen (University of Minnesota, Duluth, US)
Massimo Poesio (University of Trento, Italy)
Magnus Sahlgren (Swedish Institute of Computer Science, Sweden)
Sabine Schulte im Walde (University of Stuttgart, Germany)
Hinrich Schutze (Stuttgart University)
Suzanne Stevenson (University of Toronto, Canada)
Peter D. Turney (National Research Council, Canada)
Dominic Widdows (Google Research, US)
Yorick Wilks (University of Sheffield, UK)
Fabio Massimo Zanzotto (University of Roma "Tor Vergata", Italy)
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Contacts
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Roberto Basili
Department of Computer Science
University of Roma "Tor Vergata"
Italy
Web page (http://ai-nlp.info.uniroma2.it/basili/)
email (basili at info dot uniroma2 dot it)

Marco Pennacchiotti
Yahoo! Inc.
Santa Clara, CA
Web page (http://www.marcopennacchiotti.com/pro)
email (pennac at yahoo-inc dot com)