Call for papers:
Special issue of Computational Linguistics on
Semantic Role Labeling
Special issue website: http://www.lsi.upc.edu/~carreras/srlcl.html
BACKGROUND
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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 on
the website to find the CL Style Guide and LaTeX style files.
Articles are to be sent electronically by email in Adobe's PDF format.
Instructions will be provided at the web site.
GUEST EDITORS
-------------
Guest Editors
Lluís Màrquez, 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 (still in
the process of being finalized).
-- 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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