[Corpora-List] Security Informatics CFP: Special Issue on Text Processing

[Matthew Gerber]

Greetings,

I am pleased to announce the call for papers for a special issue of
Security Informatics:  Fusing Automatic Text Processing with Criminal
Incident Data. A brief synopsis is given below. Please see this
link<http://ptl.sys.virginia.edu/msg8u/cfp_final.pdf>(PDF) for the
submission schedule and additional information.

=====================================
Crime analysts often use an area’s historical record to visualize past
crimes (e.g., using hot-spot mapping)
and to predict locations of future criminal activity. Models for the latter
task use geographic and demographic
factors to characterize the appeal of potential crime sites, demonstrating
promising performance on real-world
prediction tasks (Fox et al., 2012; Wang and Brown, 2012). However, these
models often ignore the vast
repository of unstructured text that is freely available through, for
example, news and social media outlets.
Such information sources contain detailed descriptions of past, present,
and future events, and recent work
has shown that these descriptions can improve crime prediction performance
(Wang et al., 2012). Despite
this encouraging result, textual information remains largely unexploited
due to its vast size and unstructured
format. This special issue of Security Informatics will focus on fusing
text processing outputs (e.g., events, facts,
and opinions) with historical criminal incident data (e.g., spatio-temporal
criminal incident locations). Such
work will help bridge the current gap between unstructured text and crime
analytics (e.g., predictive policing).

In particular, we welcome high-quality submissions on the following topics:
* Extraction and geocoding (address resolution) of event locations within
unstructured text
* Extraction and normalization of event times within unstructured text
* Extraction of person/group names and sentiment from unstructured text
* Processing of “noisy” sources of unstructured text (e.g., Twitter and
weblogs)
* Fusion of the above (or other) textual information with criminal incident
data
=====================================

Sincerely,

Matthew Gerber
Lead Guest Editor
Department of Systems and Information Engineering
University of Virginia
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