icegov2012 proceedings

how language technology has been or could be used in relation to
some but not all of the processes in workflow management.
In a Workflow Management System (WfMS), the triage and
routing of cases/tasks are an important part, and automatic means
could be used to make this step more efficient. If the case or the
carrier of the case includes accessible text, language technology
could improve this process, by routing the task to a handling
officer who knows the area of the task, this could be done using
text categorization. An experiment with document routing was
carried out by [10]. They used a very limited part of the textual
resources, such as references and subject lines, in their
experiment.
When looking at businesses outside government, “trouble tickets”
have been analyzed [11]. One could see the use of the same
techniques within a government agency, here the “trouble tickets”
could for instance concern reports from the citizens about
“problems” in their neighborhood, which then could be mined
about problem areas of a specific kind in the whole community. In
the paper by Ku et al [8] information extraction was used on
police reports, newspaper articles, and victims’ and witnesses’
reports. This is an interesting example of how a system could
combine extracting information from sources on the “inside”
(police reports, victims’ and witnesses’ reports) with the outside
world (newspaper articles), and how the extraction of people,
places, vehicle names and other entities from both sides could
give a broader picture of an event.
E-mail is a common channel to contact an organization and to
initiate a case. Knutsson et al. [7] discusses the benefits of
automated e-mail answering in large organizations, as well as a
number of tools and techniques useful for e-mail processing in
eGovernment settings. Most early e-mail answering systems used
machine learning and pursued the task of text classification.
Similar to the text classification task is matching incoming e-mail
messages to a list of frequently asked questions (FAQs). Iwai et
al. [5] makes an observation that 30-40% of an e-mail flow
addresses often reoccurring topics, and presents a system for
developing an FAQ list and matching these FAQs to future emails.
Question-answering systems answer single-sentence questions. In
closed domains dealing with one or few subjects, automated FAQ
retrieval has proved practical. Recently, template based
techniques have gained more attention. Samarakoon et al. [9]
presents a help-desk application where each FAQ is associated
with hand-written text patterns that match future queries.
Statistical n-gram matching and ranking algorithms are applied. A
more “intelligent” alternative to FAQ retrieval are chatbots alias
conversational agents alias dialogue systems; these systems
maintain a dialog with the user. Deryugina [3] lists a number of
chatbot applications in customer care, which is relevant for
eGovernment.
3. CONCLUSIONS
In this paper we have discussed the use of language technology in
several different areas of workflow management. Language
technology could increase efficiency by task routing and
automatic answering, and some of the techniques even prevent
some of the citizens’ cases from becoming part of the workflow.
The use of language technology in business intelligence might
increase quality by giving organizations a better picture of the
496
information on the inside, but also what is going on outside the
government agency.
4. REFERENCES
[1] Abecker, A., Bernardi, A., Maus, H., Sintek, M. and Wenzel,
C. Information supply for business processes: coupling
workflow with document analysis and information retrieval.
Knowledge-Based Systems, 13 (2000), 271-284.
[2] Bae, H., Hu, W., Yoo, W. S., Kwak, B. K., Kim, Y., and
Park, Y. Document configuration control processes captured
in a workflow. Comput. Ind. 53, 2 (February 2004), 117-131.
[3] Deryugina, O.V. Chatterbots. Scientific and Technical
Information Processing, 37, 2 (2010), 143–147.
[4] Esposito, F., Ferilli, S., Teresa M.A. Basile, T.M.A., and Di
Mauro, N. (2008). Machine Learning for Digital Document
Processing: from Layout Analysis to Metadata Extraction,
Studies in Computational Intelligence (SCI) 90 (2008), 105–
138.
[5] Iwai, K., Iida, K., Akiyoshi, M., and Komoda, N. (2010). A
help desk support system with filtering and reusing e-mails,"
In Proceedings of the 8th IEEE International Conference on
Industrial Informatics (INDIN 2010),(Osaka, Japan, 13-16
July, 2010), 321-325.
[6] Jurafsky, D. and Martin, J. H. Speech and Language
Processing: An Introduction to Natural Language
Processing, Computational Linguistics, and Speech
Recognition, Prentice Hall, Upper Saddle River, NJ, 2008.
[7] Knutsson, O., Pargman, T. C., Dalianis, H., Rosell, M., and
Sneiders, E. Increasing the Efficiency and Quality of E-mail
Communication in eGovernment Using Language
Technology. In Proceedings of the IFIP e-government
conference (EGOV 2010), (Lausanne, Switzerland, August
29-September 2, 2010). Trauner Verlag, Linz, Austria, 45-
52.
[8] Ku, C. H., Iriberri, A., and Leroy, G. Natural Language
Processing and e-Government: Crime Information Extraction
from Heterogeneous Data Sources. In Proceedings of the 9th
Annual International Digital Government Research
Conference, (Montreal, Canada, 2008).
[9] Samarakoon, L.; Kumarawadu, S.; Pulasinghe, K. Automated
question answering for customer helpdesk applications. In
Proceedings of 6th IEEE International Conference on
Industrial and Information Systems (ICIIS 2011), (Kandy, Sri
Lanka, 16-19 August 2011) IEEE, 328-333,
[10] Soares, C. and Calejo, M. Intelligent Document Routing as a
First Step towards Workflow Automation: A Case Study
Implemented in SQL. ISoLA (1) 2010: pp. 276-284.
[11] Temprado, Y., Molinero, F. J., Garcia, C. and Gomez, J.
Knowledge Discovery from Trouble Ticketing Reports in a
Large Telecommunication Company. In Proceedings of the
2008 International Conference on Computational
Intelligence for Modelling Control & Automation (CIMCA
'08). IEEE Computer Society, Washington, DC, USA, 37-4