2005 Graduate Catalog and 2004 Annual R & D Report - Sirindhorn ...

2005 Graduate Catalog and 2004 Annual R & D Report
Sirindhorn International Institute of Technology (SIIT)
Dr. Thanaruk Theeramunkong
Associate Professor
B.Eng. in Electrical and Electronics Engineering, Tokyo Institute of Technology, Japan.
M.Eng. in Computer Science, Tokyo Institute of Technology, Japan.
D.Eng. in Computer Science, Tokyo Institute of Technology, Japan.
Areas of Specialization: Artificial Intelligence (AI), Natural Language Processing (NLP), Information Retrieval
(IR), Knowledge Data Discovery, Data Mining, Machine Learning (ML), and Intelligent Information Systems.
Research Interests:
Natural Language Processing
(1) Robust NLP and Linguistic Knowledge
Acquisition
While NLP systems are gradually becoming accepted
by a wider range of people both in academic and
business area, many difficult problems are still
unsolved. One of the important problems is how to
improve robustness and adaptiveness in NLP system,
especially how to analyze and interpret various
phrases and sentences which are ungrammatical
(also called ill-formed inputs). A user-friendly system
should be robust and flexible in that it can analyze
any well-formed and ill-formed input efficiently. The
system should also be adaptive to deal with
phrases/sentences including unseen construction and
vocabulary, for instance learning some new grammar
rules. Currently, we are focusing on both rule-based
and corpus-based approaches to cope with ill-formed
inputs and, when needed, to acquire novel linguistic
knowledge. On the increase of very large electronic
corpora, statistics obtained from such corpora are a
useful clue for this problem.
developing of efficient methods to various tasks of
text interpretation.
Knowledge Science and Engineering
(1) Knowledge Data Discovery in Database
Knowledge Data Discovery (KDD) is a rapidly growing
interdisciplinary field that merges together databases,
statistics, machine learning and other AI technologies
in order to extract useful knowledge from a largescaled
collection of data. The problems in this field
are of two general categories: (1) prediction and (2)
knowledge discovery. Knowledge discovery is a stage
prior to prediction, where information is insufficient for
prediction, such as clustering, association rules, text
mining and so on. Our study aims at finding and
implementing efficient, robust and scalable methods
in real-world situation where databases are complex,
voluminous, noisy and non-stationary. Some
interesting applications include computer-aided
education (CAI), decision support systems, and
management information systems.
(2) Text Interpretation: Information Retrieval,
Categorization and Information Extraction
In the past, most online information stored in
databases or spreadsheets. At the present time, the
majority of online information is text-based, e.g., e-
mail, news, journal articles, reports, books,
encyclopedias. These information sources are worth
but there is too much information available, and not
enough time to sort through it. Text interpretation
techniques are helpful for categorizing, filtering and
extracting information from text. Three types of text
interpretation are information retrieval, categorization,
and information extraction. We are interested in
(2) Intelligent Decision Support Systems
In business, government, and other organizations,
decision making plays an important part in
determining the landscape of tomorrow’s world.
Computer systems that assist decision-making
process are called decision support systems (DSSs).
Intelligent decision support systems (IDSSs) are
DSSs that make use of techniques emerging from the
field of artificial intelligence (AI). Our research focuses
on studying new techniques in both (1) model-driven
support systems, which are based on strong theory or
model, and (2) data-driven support systems, which
are based on database technologies and statistical
methods.
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