Final Call for papers (due July 15, 2006):
Special issue of Computational Linguistics on
Semantic Role Labeling
Special issue website: http://www.lsi.upc.edu/~carreras/srlcl.html
Submission Instructions
-----------------------
Articles submitted to this special issue must adhere to the
Computational Linguistics Style Guidelines. Please follow the link to
find the CL Style Guide and LaTeX style files. As in regular submissions
to the journal, articles are to be sent electronically by email in
Adobe's PDF format to compling@ics.mq.edu.au, indicating that the
submission is for the Special Issue on Semantic Role Labeling.
BACKGROUND
----------
The general problem of interpreting text involves the determination of
the semantic relations among the entities and the events they
participate in. Given a sentence, one formulation of the task consists
of detecting basic event structures such as "who" did "what" to
"whom", "when" and "where". From a linguistic point of view, a key
component of the task corresponds to identifying the semantic
arguments filling the roles of the sentence predicates. These
predicates are mainly lexicalized by verbs but also by some verb
nominalizations and adjectives. Typical predicate semantic arguments
include Agent, Patient, and Instrument; semantic roles may also be
found as adjuncts (e.g., Locative, Temporal, Manner, and Cause). The
related tasks of determining the semantic relations among nouns and
their modifiers, as well as prepositions and their arguments, are
clearly important for text interpretation as well, and indeed often
draw on similar role labels.
As with many areas in computational linguistcs (CL) and Natural
Language Processing (NLP), work has proceeded for decades on manually
created semantic grammars and other resources for supporting text
interpretation (e.g., [Hirst 1987], [Pustejovsky 1995], [Copestake and
Flickinger 2000]). This body of research has supported deep semantic
analysis of language input, but has the drawbacks typical of such
approaches in requiring intensive manual labor, often restricted to
narrow domains. The growth of statistical machine learning methods,
addressing these issues of the knowledge acquisition bottleneck, were
for many years limited in this area to related problems of learning
subcategorization frames [Briscoe and Carroll 1997] or classifying
verbs according to argument structure properties [Merlo and Stevenson
2001] [Schulte im Walde 2000], due to the lack of appropriate
resources to support such methods in labeling semantic roles of
arguments.
Recently, however, the compilation and manual annotation with semantic
roles of medium-large corpora - the PropBank, NomBank, and FrameNet
initiatives - has enabled the development of statistical approaches
specifically for the task of semantic role labeling (SRL). SRL,
especially focused on the labeling of verbal arguments and adjuncts,
has become a well-defined task with a substantial body of work and
comparative evaluation (e.g., see [Gildea and Jurafsky 2002],
[Surdeanu et al. 2003], [Xue and Palmer 2004], [Pradhan et al. 2005],
CoNLL Shared Task in 2004 and 2005, Senseval-3). The identification of
such event frames holds potential for significant impact in many NLP
applications, as suggested by the following works on Information
Extraction [Surdeanu et al. 2003], Question Answering [Narayanan and
Harabagiu 2004], Summarization [Melli et al. 2005], and Machine
Translation [Boas 2002]; as well, work on noun modifier relations has
been encouraging for related NLP tasks (e.g., [Moldovan and Badulescu
2005], [Rosario and Hearst 2004]). Although the use of SRL systems in
real-world applications has so far been limited, the outlook is
promising over the next several years for a spread of this type of
analysis to a range of applications requiring some level of semantic
interpretation. Moreover, the problem represents an excellent
framework to perform research on CL and NLP techniques for acquiring
and exploiting semantic relations among the different components of
the structured output to be constructed.
TOPICS
------
The call for papers of this special issue invites submissions of
articles describing novel and challenging work and results in Semantic
Role Labeling (SRL) and its applications, with emphasis on the
evaluation of qualitative and quantitative aspects that give a deep
insight on the SRL task and, in general, on the syntactico-semantic
processing of natural language. The range of topics to be covered
includes, but is not limited to:
* Novel statistical and machine learning approaches and
architectures for SRL
* Study of the relevant information/knowledge for the task
* Learning from small training sets
* Unsupervised models for SRL
* Scalability of the state-of-the-art systems
* How to make systems robust against annotation errors
* Inclusion of deep semantic information and relations
* Generalization to new corpora and to new unseen frames
* Knowledge-based approaches to SRL and comparison to the
statistical approach
* Combination of systems and approaches, specially addressing the
integration of knowledge-based and statistical views
* Study of the relation between the syntactic and semantic layers
for SRL characterization and system development
* Applications of SRL (e.g., in domains such as Q&A, MT,
Summarization, etc.)
* Evaluation: new metrics for direct evaluation and indirect
evaluations through applications
* Development of copora and resources for the task
* SRL for languages other than English
IMPORTANT DATES
---------------
Call for papers: 15 March 2006
Submission of articles: 15 July 2006
Preliminary decisions to authors: 15 November 2006
Submission of revised articles: 31 January 2007
Final decisions to authors: 15 March 2007
Final versions due from authors: 15 April 2007
Publication: Fall 2007
SUBMISSION INSTRUCTIONS
-----------------------
Articles submitted to this special issue must adhere to the
Computational Linguistics Style Guidelines. Please follow the link to
find the CL Style Guide and LaTeX style files. As in regular submissions
to the journal, articles are to be sent electronically by email in
Adobe's PDF format to compling@ics.mq.edu.au, indicating that the
submission is for the Special Issue on Semantic Role Labeling.
GUEST EDITORS
-------------
Guest Editors
Lluis Marquez, Technical University of Catalonia
Kenneth C. Litkowski, CL Research
Suzanne Stevenson, University of Toronto
Xavier Carreras, Technical University of Catalonia
GUEST EDITORIAL BOARD
---------------------
See the web site for the members of the guest editorial board.
-- Ken Litkowski TEL.: 301-482-0237 CL Research EMAIL: ken@clres.com 9208 Gue Road Damascus, MD 20872-1025 USA Home Page: http://www.clres.com
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