[Elsnet-list] Extended deadline - NAACL Workshop on Computational Neurolinguistics

Anna Korhonen alk23 at cam.ac.uk
Mon Mar 1 17:25:35 CET 2010

***  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 submissions.
   * 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 
http://naaclhlt2010.isi.edu/authors.html. 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 unitn.it, Centre for Mind/Brain Studies, 
University of Trento
   * Kai-min Kevin Chang, kaimin.chang at gmail.com, Language Technologies 
Institute, Carnegie Mellon University
   * Anna Korhonen, alk23 at cam.ac.uk, 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, University of Paris 13, 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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