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Chutima Boonthum-Denecke

Chutima Boonthum-Denecke is an Assistant Professor in the Department of Computer Science at Hampton University. Dr. Boonthum-Denecke earned her Ph.D. in Computer Science from Old Dominion in 2007; MS in Applied Computer Science from Illinois State University in 2000; and BS in Computer Science from Srinakharinwirot University in 1997. Dr. Boonthum-Denecke has been involved in several NSF-funded Broadening Participation in Computing (BPC) programs: ARTSI (Advancing Robotics Technology for Societal Impact) and STARS (Students and Technology in Academia, Research and Service) Alliances. She is also a faculty member of the Hampton University Information Assurance group. Dr. Boonthum-Denecke's research interests include artificial intelligence (natural language processing, computational linguistics), information retrieval, Web development technology, and cognitive robotics.

Publications

Applied Natural Language Processing: Identification, Investigation and Resolution

The amount of information that humans have gathered and made available to other humans is phenomenal, yet however large this repository of knowledge is, by this time tomorrow, it...

Cross-Disciplinary Advances in Applied Natural Language Processing: Issues and Approaches

Applied Natural Language Processing (ANLP) is interested in not only the creation of natural language processing approaches (i.e., tools, systems, algorithms, models, theories...

Maximizing ANLP Evaluation: Harmonizing Flawed Input

A continuing problem for ANLP (compared with NLP) is that language tends to be more natural in ANLP than that examined in more controlled natural language processing (NLP)...

Natural Language Processing Tools

This chapter discusses a subset of Natural Language Processing (NLP) tools available for researchers and enthusiasts of computer science, computational linguistics, and other...

NLP Techniques in Intelligent Tutoring Systems

Many Intelligent Tutoring Systems (ITSs) aim to help students become better readers. The computational challenges involved are (1) to assess the students’ natural language inputs...