LINGUIST List 21.151|
Sun Jan 10 2010
Calls: Computational Ling, Semantics, Cognitive Science/Denmark
Editor for this issue: Kate Wu
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Compositionality and Distributional Semantic Models
Message 1: Compositionality and Distributional Semantic Models
From: Alessandro Lenci <alessandro.lenciling.unipi.it>
Subject: Compositionality and Distributional Semantic Models
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Full Title: Compositionality and Distributional Semantic Models
Short Title: DistComp 2010
Date: 16-Aug-2010 - 20-Aug-2010
Location: Copenhagen, Denmark
Contact Person: Alessandro Lenci
Meeting Email: alessandro.lenciling.unipi.it
Web Site: http://clic.cimec.unitn.it/roberto/ESSLLI10-dsm-workshop/
Linguistic Field(s): Cognitive Science; Computational Linguistics; Semantics
Call Deadline: 12-Apr-2010
Workshop organized as part of the European Summer School on Logic, Language and
Information ESSLLI 2010 (http://esslli2010cph.info/),
August 16-20 2010 (ESSLLI second week), Copenhagen
Alessandro Lenci (alessandro.lenciling.unipi.it)
Roberto Zamparelli (roberto.zamparelliunitn.it)
Call for Papers
In the last ten years distributional semantic models (DSMs), such as LSA, HAL,
etc. have been quite successful at addressing semantic similarity, lexical
ambiguity, lexical entailment, verb selectional restrictions and other word
level relations. In this class of models the meaning of a content word is
represented in terms of a distributed vector recording its pattern of
cooccurrences (sometimes, in specific syntactic relations) with other content
words within a corpus. Different types of semantic tasks and phenomena are then
modeled in terms of linear algebra operations on distributional vectors.
A central question about DSMs is whether and how distributional vectors can also
be used in the compositional construction of meaning for constituents larger
than words, and ultimately for sentences or discourses -- the traditional
domains of denotation-based formal semantics. Being able to model key aspects of
semantic composition represents a crucial condition for DSMs to provide a more
general model of meaning. Conversely, distributional representations might help
to model those aspects of meaning that notoriously challenge semantic
compositionality, such as semantic context-sensitivity, polysemy, predicate
The workshop aims to bring together researchers in formal and computational
semantics to chart this largely unexplored territory.
The following is a non-exhaustive list of issues that submissions to the
workshop might address:
- Is it possible, and useful, to use Distributional Semantic Models to assign a
semantic representation to constituents (e.g. phrases, propositions, etc.)?
- How can the notion of predication be interpreted in Distributional Semantic
- Can Distributional Semantic Models provide an alternative way to solve puzzles
concerning predicate-argument composition (e.g. type-mismatch, coercion, etc.)?
- Can we use distributional models to capture argument structure and its
alternations, or the Aktionsart of a complex predicates?
- Can Distributional Semantic Models apply below the word level, characterizing
the notions of morpheme productivity and morpheme composition? (e.g. can we
capture distributionally the decreasingly compositional meanings of
"inter+breed", "inter+act", "inter+view"?)
- Can Distributional Semantic Models be used to model word meaning interactions
in modificational contexts, such as figurative interpretations,
context-sensitive sense shifts (e.g. "fast car" vs. "fast guitarist"), etc.?
- How can polysemy and ambiguity be modelled in Distributional Semantic Models?
Which types of ambiguity could be resolved in a DSM-based compositional process?
Can this help the task of resolving lexical and textual entailments?
- What is the right relation between the interpretation functions of formal
semantics and the distributional semantic representation these models provide?
- What should be the most insightful relation between distributional semantic
representations of content words and the meaning of the function words that
combine with them?
- Can DSMs provide distributional correlates of constructions and lexical
classes that are known to be relevant in formal semantics? (e.g. distributional
models of bare plurals, the count vs. mass distinction, generic vs. episodic
- Similarly, can these models capture different types of reference (e.g. nouns
or noun phrases that refer to objects, to kinds, to events, to facts or
Authors are invited to submit an EXTENDED ABSTRACT for a 20-minute
presentation (followed by a 10 minute discussion).
- not exceed 3 pages, including all figures and references.
- be in pdf
- be submitted using the Easychair interface at the URL:
- be anonymous and, therefore, accompanied by information containing:
author name(s), affiliation(s), e-mail and postal address(es), and the title of
the paper (these can be filled in at the Easychair site)
The submissions will be reviewed anonymously by the workshop's programme
committee. Details will be specified on the workshop homepage
The abstracts accepted for presentation will appear in the ESSLLI web site. We
are inquiring about the possibility to publish the final workshop papers, either
as part of the ESSLLI proceedings or in a separate form.
Marco Baroni (University of Trento)
Gemma Boleda (Universitat Politecnica de Catalunya)
Katrin Erk (University of Texas)
Stefan Evert (University of Osnabrueck)
Graham Katz (Georgetown University)
Alessandro Lenci (University of Pisa) (co-organizer)
Louise McNally (Universitat Pompeu Fabra)
James Pustejovsky (Brandeis University)
Sebastian Pado (IMS, Stuttgart)
Magnus Sahlgren (Swedish Institute of Computer Science)
Gabriel Sandu (University of Helsinki)
Sabine Schulte im Walde (University of Stuttgart)
Peter Turney (National Research Council Canada)
Roberto Zamparelli (University of Trento) (co-organizer)
All workshop participants, including the authors, are required to register for
Apr 12, 2010: Deadline for submission
May 24, 2010: Notification
Jun 1, 2010: Deadline for early registration to ESSLLI
June 30, 2010: Final programme
August 16-20, 2010: Workshop
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