Appel: NAACL Workshop on Computational Neurolinguistics (Nouvelle date limite de soumission)

Thierry Hamon thierry.hamon at UNIV-PARIS13.FR
Sat Mar 6 09:14:02 UTC 2010

Date: Wed, 3 Mar 2010 00:53:26 +0100
From: Thierry Poibeau <thierry.poibeau at>
Message-Id: <F2628FF0-A288-4929-B6B5-60CC3A131B3B at>

***  NEW: submission deadline extended to March 10, 2010  ***

  Call for Papers

  5 or 6 June, NAACL-HLT 2010, Los Angeles


The first Workshop on Computational Neurolinguistics will be held at
 NAACL next June in Los Angeles. We welcome submissions on the
 computational treatment of any aspect of language, that either make
 use of neural recordings or of biologically realistic neuronal
 models. To encourage submissions from the broadest community, the
 organisers are releasing two neural activity datasets, fMRI and EEG,
 described below. Submissions should be made through the NAACL system,
 with a deadline of March 1st, 2010:


Computational neurolinguistics is an emerging research area which
integrates recent advances in computational linguistics and cognitive
neuroscience, with the objective of developing cognitively plausible
models of language and gaining a better understanding of the human
language system. It builds on research in decoding cognitive states
from recordings of neural activity, and computational models of
lexical representations and sentence processing. Published work in
this area includes the discovery of semantic features in neural
activity (Mitchell et al, 2008), using brain signals for the relative
evaluation of corpus semantic models (Murphy et al, 2009), and
recognizing the semantics of adjective-noun meaning composition (Chang
et al, 2009).

On-going research focuses on a number of topics such as brain-computer
interfaces to provide dictation systems for paraplegic patients, and
algorithms to perform tagging and shallow parsing of neural activity
recorded during sentence comprehension. Both computational linguistics
and neuroscience stand to gain from these techniques. In computational
linguistics, the cognitive plausibility of language models has
primarily been evaluated against collections of subjective intuitions
(e.g. semantic feature norms, grammaticality judgments, corpus
annotations, dictionaries). Evaluation of the large body of
Computational Linguistics work based on data driven distributional
approaches has also relied on hand-crafted resources such as WordNet
or data sets manually tagged with a predefined list of
categories. Comparison with neural data may provide a more objective
yardstick for both models and resources. And in brain imaging,
language-related research has often been limited to relatively coarse
analyses (e.g. high level features such as animacy or part-of-speech)
but now computational neurolinguistic methods have leveraged the
richness of corpus-based descriptions to extract finer-grained
representations for single lexemes.

Advances in computational neurolinguistics require close collaboration
between computational linguists and neuroscientists. To this end, an
interdisciplinary workshop can play a key role in advancing existing
and initiating new research. We hope that it will attract an
interdisciplinary target audience consisting of computational
linguists, machine learning researchers, computational neuroscientists
and cognitive scientists.

Topics of Interest
 * Computational Linguistic Focus
       o Word-level analyses (e.g. corpus semantic models, lexica,
         lexical relations and ontologies, parts-of-speech, word
         senses, morphology)
       o Phrase-level analyses (e.g. word compounds, meaning
         composition in multi-word expressions)

 * Machine Learning Focus
       o Decoding of cognitive states from neural activity
       o Feature selection and data mining techniques for decoding
         linguistic information

 * Neural Science Focus
       o Brain imaging techniques: fMRI, EEG, MEG, NIRS, including
         cross-modality analysis (e.g. combining fMRI and EEG)
       o Localizing Regions of Interest (e.g. identify the roles /
         functions of brain regions)

 * Cognitive Science Focus
       o Comparisons with behavioral (e.g. priming experiments,
         eye-tracking, self-paced reading) and elicited data
         (e.g. semantic feature norms)

       o Biologically plausible connectionist approaches

Shared Data-Sets

Submissions based on any data-sets or tasks are welcomed, and
originality of approach is encouraged. However, to assist researchers
who are new to this topic, we are providing the data used in Mitchell
et al. (2008) and Murphy et al. (2009), as well as a number of sample
shared tasks. Submissions are welcome that follow the tasks in whole
or in part, or simply to use them as an evaluation baseline for their
own work. Performance will not be independently validated by the
organizers, and will only be one of the criteria used to select among

 * The CMU fMRI data-set of 60 concrete concepts, in 12 categories,
   collected while nine English speakers were presented with 60 line
   drawings of objects with text labels and were instructed to think
   of the same properties of the stimulus object consistently during
   each presentation. For each concept there are 6 instances of ~20k
   neural activity features (brain blood oxygenation levels):

 * The Trento EEG data-set for 60 concept concepts, in 2 categories
   (work tools and land mammals), collected while seven Italian
   speakers were silently naming photographic images that represent
   these concepts. For each concept there are 6 instances of ~15k
   neural activity features (spectral power in voltage signals):
             Sample Shared Tasks

As noted above, submissions on any task are welcomed, and these tasks
are primarily intended to provide a possible starting point for
researchers who are new to the topic.

 * Concept-pair neural discrimination task: For two concepts randomly
   left out of training, teach a classifier to match recorded neural
   data to the correct lexeme. This may be achieved by taking
   advantage of corpus-based models of word meaning, as in published
   research, or otherwise. This task is based on the evaluation method
   used with fMRI data in Mitchell et al. (2008), and replicated with
   EEG data in Murphy et al. (2009).

 * Corpus semantic model evaluation task: Teach a classifier to
   predict the neural activity observed for single concepts, based on
   each of several corpus semantic models. The average similarity
   between observed activity and predicted activity over all concepts
   can be taken as metric of corpus model fidelity.

Important Dates
 * March 10, 2010: Deadline for submission of workshop papers
 * March 30, 2010: Notification of acceptance
 * April 12, 2010: Camera-ready papers due
 * June 5 or 6, 2010: Workshop date


Authors are invited to submit full papers on original, unpublished
work in the topic area of this workshop via the NAACL submission site:

Submissions should be formatted using the NAACL 2010 stylefiles, with
blind review and not exceeding 8 pages plus an extra page for
references. The stylefiles are available at The PDF files will be
submitted electronically through the NAACL submission system, the link
will be available later. Each submission will be reviewed at least by
two members of the programme committee. Accepted papers will be
published in the workshop proceedings. Dual submissions to the main
NAACL 2010 conference and this workshop are allowed; if you submit to
the main session, indicate this when you submit to the workshop. If
your paper is accepted for the main session, you should withdraw your
paper from the workshop upon notification by the main session.

 * Brian Murphy, brian.murphy at, Centre for Mind/Brain Studies,
   University of Trento
 * Kai-min Kevin Chang, kaimin.chang at, Language Technologies
   Institute, Carnegie Mellon University
 * Anna Korhonen, alk23 at, Computer Laboratory, University of

Program Committee
 * Afra Alishahi, Saarland University, Germany
 * Ben Amsel, University of Toronto, Canada
 * Stefano Anzellotti, Harvard University, USA
 * Colin Bannard, University of Texas Austin, USA
 * Marco Baroni, University of Trento, Italy
 * Gemma Boleda, Universitat Politècnica de Catalunya, Spain
 * Ina Bornkessel, Max Planck Leipzig, Germany
 * Augusto Buchweitz, Carnegie Mellon University, USA
 * George Cree, University of Toronto, Canada
 * Barry Devereux, University of Cambridge, UK
 * Katrin Erk, University of Texas Austin, USA
 * Stefan Evert, Unversity of Osnabrück, Germany
 * Adele Goldberg, Princeton University, USA
 * Chu-Ren Huang, Hong Kong Polytechnic University, Hong Kong
 * Aravind Joshi, University of Pennsylvania, USA
 * Marcel Just, Carnegie Mellon University, USA
 * Frank Keller, University of Edinburgh, UK
 * Charles Kemp, Carnegie Mellon University, USA
 * Mirella Lapata, University of Edinburgh, UK
 * Chia-Ying Lee, Academia Sinica, Taiwan
 * Roger Levy, University of California Sand Diego, USA
 * Angelika Lingnau, University of Trento, Italy
 * Brad Mahon, University of Rochester, USA
 * Robert Mason, Carnegie Mellon University, USA
 * Diana McCarthy, Lexical Computing Ltd, UK
 * Ken McRae, University of Western Ontario, Canada
 * Tom Mitchell, Carnegie Mellon University, USA
 * Fermin Moscoso del Prado Martin, University of Provence, France
 * Sebastian Padò, University of Stuttgart, Germany
 * Francisco Periera, Princeton University, USA
 * Massimo Poesio, University of Trento, Italy
 * Thierry Poibeau, CNRS, France
 * Dean Pomerleau, Intel Labs Pittsburgh, USA
 * Ari Rappoport, Hebrew University of Jerusalem, Israel
 * Brian Roark, Oregeon Health & Science University, USA
 * Kenji Sagae, University of Southern California, USA
 * Hinrich Schütze, Stuttgart University, Germany
 * Sabine Schulte im Walde, University of Stuttgart, Germany
 * Svetlana Shinkareva, University of South Carolina, USA
 * Nathaniel Smith, University of San Diego, USA
 * Aline Villavicencio, Federal University of Rio Grande do Sul, Brazil
 * David Vinson, University College London, UK
 * Yang ChinLung, City University of Hong Kong, China

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