Journal of Natural Language Engineering
Special Issue on:

Distributional Lexical Semantics

Electronic submission is available!!!.
Authors are invited to submit their papers electronically through the following Web Submission Page.

The deadline for submitting an article to the Special Issue has been delayed to July 15th, 2009.

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.


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,

  • 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
  • Psychological and cognitive theories of semantic space models
  • Applications in the humanities and social sciences
  • Application of distributional apporaches in :
    • Word sense disambiguation and discrimination
    • Selectional preference induction
    • Acquisition of lexicons and linguistic patterns
    • Conceptual clustering
    • Kernels methods for NLP (e.g. relation extraction and textual entailment)
    • Quantitative extensions of Formal Concept Analysis
    • modeling of linguistic and ontological knowledge

Important Dates

Call for Paper: 2 March 2009
Submissions deadline: (new date) 15 July 2009. (old date 30 June 2009)
First Evaluation Results: October 2009
Second Submission: January 2010
Final Acceptance: March 2010
Special Issue: May 2010


Articles submitted to this special issue must adhere to the Journal Style Guidelines. Style Guide and LaTeX style files can be found
here. We encourage authors to keep their submissions below 30 pages.

Authors are invited to submit their papers electronically through the following
Web Submission Page.

Submission is in two phases. First authors are requested to register and submit the title and abstract of their paper. Then a second protected access allows registered authors to upload the full version of their papers in PDF. Please contact the editors for any problem.

Editorial Board

Guest Editors

Roberto Basili, University of Roma Tor Vergata, Italy
Marco Pennacchiotti, Yahoo! Inc., Santa Clara, US

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 (DERI, National University of Ireland, Galway)
John A. Bullinaria (University of Birmingham, UK)
Rodolfo Delmonte (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)
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)


Roberto Basili   Marco Pennacchiotti
Department of Computer Science Yahoo! Inc.
University of Roma "Tor Vergata" Santa Clara, CA
Italy USA
basili (at) info (dot) uniroma2 (dot) it pennac (at) yahoo-inc (dot) com
Web page Web page

Legolas 2009