posted by user: grupocole || 1854 views || tracked by 4 users: [display]

EVENTS 2013 : The 1st Workshop on EVENTS: Definition, Detection, Coreference, and Representation

FacebookTwitterLinkedInGoogle

Link: https://sites.google.com/site/cfpwsevents/home
 
When Jun 14, 2013 - Jun 14, 2013
Where Atlanta, USA
Submission Deadline Mar 15, 2013
Notification Due Apr 5, 2013
Final Version Due Apr 22, 2013
Categories    NLP
 

Call For Papers



Endorsed by SIGLEX and SIGSEM.

‪The 1st Workshop on EVENTS: Definition, Detection, Coreference, and
Representation
Held in Conjunction with NAACL-2013

https://sites.google.com/site/cfpwsevents/home

Workshop Description

The definition and detection of events have their roots in philosophy and
linguistics, with seminal works by Davidson (1969, 1985), Quine (1985) and
Parsons (1990), and have long been a subject of study. However, the NLP
community has yet to achieve a consensus on the treatment of events, in spite
of its critical importance to several areas in natural language processing, such
as topic detection and tracking (Allan et al., 1998), information extraction
(Humphreys et al., 1997), question answering (Narayanan and Harabagiu,
2004), textual entailment (Haghighi et al., 2005), and contradiction detection
(de Marneffe et al., 2008). Most attempts to provide annotation of event
coreference have been limited to specific scenarios or domains, as in LDC’s
ACE and Machine Reading event annotation, (Humphreys et al., 1997; Bagga
and Baldwin, 1999; He, 2007). The recent OnotoNotes annotations include
more general event mentions and coreference, but mainly identify
coreferences between verbs and nominalizations (Pradhan, 2007). Events are
also a crucial element of TimeML, or temporal relation annotation, which have
an overlapping but slightly different approach (Pustejovsky, et. al., 2010).
Truly comprehensive event detection must encompass the detection of events
and their subevents, as well as bridging references (Poesio and Artstein,
2005; 2008). This type of event representation is clearly related to the
information available in lexical resources such as PropBank, VerbNet and
FrameNet, but goes well beyond anything they currently capture. Bejan and
Harabagiu (2010) have recently offered broader event coreference annotation
for evaluation purposes, which have been revised and extended by Lee, et.al,
(2012). The organizers are themselves involved in event coreference projects
for medical informatics and for deep natural language understanding. The
time is ripe to bring together interested parties for a serious discussion of
appropriate guidelines, resources, and processes for defining and detecting
events and their coreferences, and how they should be represented. James
Pustejovsky has agreed to give the keynote address.

This is a genuine “working” workshop on this topic. The organizers, with the
assistance of the program committee, have organized a small shared
annotation task on event mention and coreference annotation. The purpose
of this annotation is to have all participants look at the principal phenomena
of interest and apply their preferred annotation scheme to it. The resulting
annotations will be analyzed for agreements and disagreements which will be
discussed thoroughly, with examples, in working sessions and panels at the
workshop, with the aim of achieving a consensus on the handling of
disagreements. Annotation data is available for participants interested in
participating, as described below.

The sessions and panels are expected to focus on the following topics:

- Foundations: What are Events? Definition and Recognition

- Coreference: When are Two Events the Same?

- Representation: How Best to Represent Events and Event Groups?

The workshop also invites both full papers and short papers for a poster
presentation on any of these topics


‪Shared Annotation Task‬
The annotation task will call for identifying instances of event mentions and
coreference links,
including bridging references, with an optional layer of post-coreference
inference. A small
number of texts will be distributed to participants to be annotated. Each
team will use their
own guidelines for this annotation, which may or may not be domain
dependent, and may
or may not include bridging references. If teams choose to limit the events
they include,
this will require an explanation of why based on a domain event list/hierarchy
for the data.
Teams also have to agree to share their guidelines with other participants.

‪Important Dates‬
Jan 30, 2013 Start distributing data for annotation to participants
Mar 15, 2013 Paper submissions due date (23:59 UTC-11) Deadline
Extended.
Mar 31, 2013 Data annotations are returned
Apr 5, 2013 Notification of acceptance
Apr 22, 2013 Camera-ready papers due
Apr 30, 2013 Analysis of data annotations completed
May 15, 2013 Schedule and topics for working sessions, panels,
distributed



‪Program Committee‬

Marjorie Freeman (BBN)
Alan Goldschen (Mitre)
Kira Griffit (LDC)
Heng Ji (CUNY)
Boyan Onyshkevych (DOD)
Marta Recasens (Stanford University)
Stephanie Strassel (LDC)
Mihai Surdeanu (University of Arizona)
Sara Tonelli, (Fondazione Bruno Kessler (FBK), Trento)
Ben Van Durme (JHU-COE)

Workshop Organizers‬
Eduard Hovy, Carnegie Mellon University, USA
Teruko Mitamura, Carnegie Mellon University, USA
Martha Palmer, University of Colorado, USA



Related Resources

ICLR 2024   International Conference of Learning Representations
JANT 2024   International Journal of Antennas
KR 2024   Principles of Knowledge Representation and Reasoning
CST 2024   11th International Conference on Advances in Computer Science and Information Technology
DS 2024   Discovery Science 2024
TAL-ALD 2024   Special issue of the journal Traitement Automatique des Langues (TAL) Abusive Language Detection : Linguistic Resources, Methods and Applications
IJME 2024   International Journal of Microelectronics Engineering
RepL4NLP 2024   9th Workshop on Representation Learning for NLP
CLBD 2024   5th International Conference on Cloud and Big Data
SI AID 2024   SPECIAL ISSUE on Adaptive Intrusion Detection System using Machine Learning in Wireless Sensor Networks