Christopher S.G. Khoo

Bio-description

Chris joined Nanyang Technological University (NTU) in Jan. 1996. At NTU, he teaches courses on data mining, web-based information systems, information architecture, cataloging & classification, knowledge organisation, and evaluation of information services.

Before joining NTU, he worked for eight years at the National University of Singapore Library, in various departments (Cataloguing Dept, Science/Tech Reference, Science Library, and Medical Library) and in various roles (cataloger, reference librarian, and serials librarian).

He obtained his MSc in Library & Information Science at the University of Illinois, Urbana-Champaign, in 1988. He proceeded to Syracuse University to work for his PhD in 1991. His PhD thesis was on "Automatic Identification of Causal Relations in Text and Their Use for Improving Precision in Information Retrieval." He was the editor of the Singapore Journal of Library & Information Management, from 1997 to 2002.

Hobbies: Chris plays the violin and piano, listens to classical music, and is an avid reader of science fiction and fantasy.

Research Interests

Chris' main research interests are in

• Natural language processing & text mining
  • Information extraction
  • Identification of cause-effect relations
  • Multidocument summarization
  • Automatic sentiment analysis and categorization
• Knowledge organization
  • Taxonomies and metadata for content organization and knowledge discovery in organizational portals and learning object repositories
  • Ontologies
  • Human categorization behaviour

Publications

Current Projects

• Natural Language Processing
• Knowledge Organisation
• Library & Informaton Science Education

Natural Language Processing

Title of Project: Intelligent Search Agent for Information Extraction and Synthesis on the Web

Investigators: Chris Khoo, Dion Goh, Jin-Cheon Na, Syin Chan (School of Computer Engineering)

Funding: AcRF, S$74K

Description

The project will develop a prototype search agent that performs information extraction and synthesis on the Web. The system will be designed to be usable by end-users to extract relevant information from multiple Web sites and integrate the information into a multi-document summary. This project combines two core technologies: 1) information extraction, and 2) information integration/multi-document text summarization.

Most Web search engines and intelligent search agents merely identify potentially relevant documents on the Web without actually extracting the relevant information from the text of the documents. Information extraction systems developed so far require large training sets, are usable only by experts and take a long time to train. The study seeks to develop an intelligent information extraction system that can be trained by ordinary users using a small number of examples.The project will also develop techniques for integrating information extracted from multiple sources into a multi-document summary to provide an overview of the subject area and to aid in knowledge discovery and knowledge acquisition.

The project has been divided into two smaller projects:

• Mining disease-treatment knowledge from a medical database for knowledge discovery and evidence-based medicine
• Multi-document Summarizaton of Research Abstracts

Papers

• Khoo, C., & Na, J.C. (2006). Semantic Relations in Information Science. Annual Review of Information Science and Technology, 40, 157-228.
• Khoo, C., Chan, S., & Niu, Y. (2002). The many facets of the cause-effect relation. In R.Green, C.A. Bean & S.H. Myaeng (Eds.), The semantics of relationships: An interdisciplinary perspective (pp. 51-70). Dordrecht: Kluwer
• Khoo, C., & Myaeng, S.H. (2002). Identifying semantic relations in text for information retrieval and information extraction. In R.Green, C.A. Bean & S.H. Myaeng (Eds.), The semantics of relationships: An interdisciplinary perspective (pp. 161-180). Dordrecht: Kluwer.
• Khoo, C., Myaeng, S.H., & Oddy, R. (2001). Using cause-effect relations in text to improve information retrieval precision. Information Processing and Management, 37(1), 119-145.
• Khoo, C., Kornfilt, J., Oddy, R., & Myaeng, S.H. (1998). Automatic extraction of cause-effect information from newspaper text without knowledge-based inferencing. Literary & Linguistic Computing, 13(4), 177-186.
• Khoo, C. (1997). The use of relation matching in information retrieval. LIBRES: Library and Information Science Research Electronic Journal [Online], 7(2). Available at: http://aztec.lib.utk.edu/libres/libre7n2/.

Title of Project: Mining disease-treatment knowledge from a medical database for knowledge discovery and evidence-based medicine

Investigators: Chris Khoo, Jin-Cheon Na, Syin Chan

Description

This project seeks to extend automatic information extraction technology and apply it to the medical domain to extract disease-treatment information from medical abstracts to support evidence-based medicine and knowledge discovery. Current information extraction systems make use of linguistic patterns and pattern matching to identify the pieces of information to extract from unstructured text. The extracton patterns are often constructed automatically by applying a supervised learning technique on a set of manually annotated training text. This project seeks to develop a technique to construct the information extraction patterns without manual annotation of text by performing text mining, automatic text annotation and pseudo-supervised learning. The objectives of the project are:

• To develop an effective method to mine information extraction patterns in a medical database
• To develop a method to construct information extraction patterns using pseudo-supervised learning and automated annotation of training text
• To develop a disease-treatment ontology to model and represent treatment information found in medical abstracts, and to summarize the information to support evidence-based medicine
• To develop a method for inferencing and synthesizing potential new knowledge from information extracted from disparate medical articles

Papers

• Khoo, C., Na, J.C., Wang, W., & Chan, S. (2008). Pattern mining for information extraction using lexical, syntactic and semantic information: Preliminary results. In Asia Information Retrieval Symposium 2008 (pp. 684-689). Berlin: Spinger.
• Khoo, C., Na, J.C., Wang, W., & Chan, S.. (2007). Modeling cancer treatment information with an ontology. In International Conference on Future of Knowledge Organization in the Networked Environment (IKONE 2007) (pp. 255-268). Bangalore: Indian Statistical Institute.
• Lee, C.H., Na, J.C., & Khoo, C. (2006). Towards ontology enrichment with treatment relations extracted from medical abstracts. In 9th International Conference on Asian Digital Libraries, ICADL 2006: Proceedings (Lecture Notes in Computer Science, 4312/2006, pp. 419-428). Berlin: Springer.
• Lee, C.H., Khoo, C., & Na, J.C. (2004). Automatic identification of treatment relations for medical ontology learning: An exploratory study. In I.C. McIlwaine (Ed.), Knowledge Organization and the Global Information Society: Proceedings of the Eighth International ISKO Conference (pp. 245-250). Wurzburg, Germany: Ergon Verlag.
• Lee, C.H., Na, J.C., & Khoo, C. (2003). Ontology learning for medical digital libraries. In Digital Libraries: Technology and Management of Indigenous Knowledge for Global Access: 6th International Conference on Asian Digital Libraries,ICADL 2003 (pp. 302-305). (Lecture Notes in Computer Science, 2911). Berlin: Springer-Verlag.
• Na, J.C., Khoo, C., Lee, C.H. (2003). Automatic extraction of ontology relations from medical abstracts. In Taxonomy By Any Other Name - State of the Art Implementation of Classification Research in Information Technologies: 14th Annual ASIST SIG CR Workshop. American Society for Information Science & Technology.
• Khoo, C., Chan, S., & Niu, Y. (2000). Extracting causal knowledge from a medical database using graphical patterns. In ACL-2000: 38th Annual Meeting of the Association for Computational Linguistics, 1-8 October 2000, Hong Kong (pp. 336-343). New Brunswick, NJ: Association for Computational Linguistics.
• Khoo, C., Chan, S., Niu, Y., & Ang, A. (1999). A method for extracting causal knowledge from textual databases. Singapore Journal of Library & Information Management, 28, 48-63.

Title of Project: Multi-Document Summarizaton of Research Abstracts

Investigators: Chris Khoo, Dion Goh, Shiyan Ou

Description

Multi-document summarization is the process of condensing, not just one document, but a set of related documents into a single summary. This study aims to develop an automatic method for summarizing sets of research abstracts that may be retrieved by an information retrieval or digital library system in response to a user query.

Most retrieval systems and Web search engines display a ranked list of titles and extracts as the search result for a user query. However, the user has patience to scan only a small number of records, usually in the range of 10 to 30. It is desirable to provide the user with an overview of the content of the documents retrieved and help the user identify relevant documents more effectively. Multi-document summarization is one answer--it generates a summary of a set of documents, providing a domain overview of the topic based on the documents retrieved, indicates similar information across documents, unique information in individual documents, and relationships between pieces of information across documents. It can allow the user to zoom in for more details on particular aspects of interest, and zoom into the individual single-document summaries.

The initial study focuses on summarizing sets of sociology dissertation abstracts retrieved from the Dissertation Abstracts International database. The work is being extended to summarizing journal article abstracts in various fields.

Papers

• Ou, S., Khoo, C., & Goh, D. (in press). Design and development of a concept-based multi-document summarization system for research abstracts. Journal of Information Science.
• Ou, S., Khoo, C., & Goh, D. (2007). Automatic multi-document summarization of research abstracts: Design and user evaluation. Journal of the American Society for Information Science & Technology, 58(10), 1419-1435.
• Ou, S., Khoo, C., & Goh, D. (2007). Multi-document summarization focusing on extracting and integrating similarities and differences among documents. In Proceedings of the 2007 International Conference Recent Advances in Natural Language Processing (RANLP-2007) (pp. 442-446).
• Ou, S., Khoo, C., & Goh, D. (2006). Multi-document summarization of news articles using an event-based framework. ASLIB Proceedings, 58(3), 197-217.
• Ou, S., Khoo, C., & Goh, D. (2006). Automatic multi-document summarization for digital libraries. In Proceedings of the Asia-Pacific Conference on Library & Information Education & Practice 2006 (A-LIEP 2006) (pp. 72-82). Singapore: School of Communication & Information, Nanyang Technological University.
• Ou, S., Khoo, C., & Goh, D. (2005). Constructing a taxonomy to support multi-document summarization of dissertation abstracts. Journal of Zhejiang University: Science 6A(11), 1258-1267.
• Ou, S., Khoo, C., & Goh, D.H. (2005). Development and evaluation of a multi-document summarization method focusing on research concepts and their research relationships. In Digital Libraries: Implementing Strategies and Sharing Experiences: 8th International Conference on Asian Digital Libraries, ICADL 2005: Proceedings (pp. 283-292). (Lecture Notes in Computer Science, 3815). Berlin: Springer-Verlag.
• Ou, S., Khoo, C., & Goh, D.H. (2005). A multi-document summarization system for sociology dissertation abstracts: Design, implementation and evaluation. In Research and Advanced Technology for Digital Libraries, 9th European Conference, ECDL 2005 (pp. 450-461). (Lecture Notes in Computer Science, 3652). Berlin: Springer-Verlag.
• Ou, S., Khoo, C., Goh, D.H., & Heng, H.Y. (2004). Automatic discourse parsing of sociology dissertation abstracts as sentence categorization. In I.C. McIlwaine (Ed.), Knowledge Organization and the Global Information Society: Proceedings of the Eighth International ISKO Conference (pp. 345-350). Wurzburg, Germany: Ergon Verlag.
• Ou, S., Khoo, C., & Goh, D. (2003). Multi-document summarization of dissertation abstracts using a variable-based framework. In ASIST 2003: Proceedings of the 66th ASIST Annual Meeting (vol. 40, pp. 230-239). Medford, NJ: Information Today.
• Ou, S., Khoo, C. Goh, D., & Heng, H.Y. (2003). Discourse parsing of sociology dissertation abstracts using decision tree induction. In Taxonomy By Any Other Name – State of the Art Implementation of Classification Research in Information Technologies: 14th Annual ASIST SIG CR Workshop. American Society for Information Science & Technology.
• Khoo, C., Ou, S., & Goh, D. (2002). A hierarchical framework for multi-document summarization of dissertation abstracts. In E.P. Lim, et al. (Eds.), Digital Libraries: People, Knowledge, and Technology: 5th International Conference on Asian Digital Libraries, ICADL 2002, Singapore (pp. 99-110). Berlin: Springer.

Title of Project: Automatic Sentiment Analysis & Categorization

Investigators: Chris Khoo, Jin-Cheon Na, Tun Thura Tet, Syin Chan (NTU School of Computer Engineering)

Description

The objective of the project is to develop techniques for automated or computer-assisted sentiment analysis of various genres of text. Sentiment refers to a person’s feeling, emotion or attitude toward a subject, and can cover a variety of emotional dispositions (e.g. anger, admiration, dislike, eagerness, etc.). The appraisal theory (Rothery, 1997; Martin, 1995), which is based on the principles of Systemic Functional Linguistics, is adopted as a framework for the study for its clear and comprehensive explication of how sentiment is expressed in language. It divides appraisal into Attitude, Engagement and Graduation, with Attitude further divided into Affect (emotion), Judgment (ethical/social evaluation) and Appreciation (aesthetic assessment). Current work is focused on:

• automatic categorization of product reviews into positive (favorable/recommended) versus negative (unfavorable/not recommended) sentiment
• development of a sentiment meta-search engine to identify documents and document snippets reporting product reviews and categorizing them into positive and negative reviews
• automatic sentiment analysis of polical news articles using a framework based on the appraisal theory.

Papers

• Nourbakhsh, A., Khoo, C., & Na, J.C. (in press). A framework for sentiment analysis of political news articles. To be presented at the ICA (International Communication Association) Conference 2008.
• Thet, T.T., Na, J.C., & Khoo, C. (2007). Automatic classification of Web search results: Product review vs. non-review documents. In 10th International Conference on Asian Digital Libraries (ICADL 2007): Proceedings (Lecture Notes in Computer Science, vol. 4822, pp. 65-74). Berlin: Springer.
• Thet, T.T., Na, J.C., & Khoo, C. (2007). Filtering product reviews from Web search results. In Proceedings of the ACM Symposium on Document Engineering 2007 (pp. 196-198). New York: ACM.
• Na, J.C., Khoo, C., & Chan, S. (2006). A sentiment-based meta search engine. In Proceedings of the Asia-Pacific Conference on Library & Information Education & Practice 2006 (A-LIEP 2006) (pp. 83-89). Singapore: School of Communication & Information, Nanyang Technological University.
• Na, J.C., Khoo, C., & Wu, P.H.J. (2005). Use of negation phrases in automatic sentiment classification of product reviews. Library Collections, Acquistions & Technical Services, 29(2), 180-191.
• Na, J.C., Khoo, C., Chan, S., & Hamzah, N. (2005). Sentiment-based search in digital libraries. In Proceedings of the Joint Conference on Digital Libraries (JCDL) 2005 (pp. 143-144). Berlin: Springer-Verlag.
• Na, J.C., Sui, H., Khoo, C., Chan, S., & Zhou, Y. (2004). Effectiveness of simple linguistic processing in automatic sentiment classification of product reviews. In I.C. McIlwaine (Ed.), Knowledge Organization and the Global Information Society: Proceedings of the Eighth International ISKO Conference (pp. 49-54). Wurzburg, Germany: Ergon Verlag.
• Sui, H., Khoo, C., & Chan, S. (2003). Sentiment classification of product reviews using SVM and decision tree induction. In Taxonomy By Any Other Name - State of the Art Implementation of Classification Research in Information Technologies: 14th Annual ASIST SIG CR Workshop. American Society for Information Science & Technology.

Knowledge Organization

Title of Project: Content Organization of Organizational Websites, Portals and Repositories Using Taxonomies

Investigators: Abdus Sattar Chaudhry, Chris Khoo, Zhonghong Wang

Description

This is a series of studies on how organizational web sites, portals and repositories should be organized using taxonomies to support browsing, finding, knowledge sharing, knowledge discovery and knowledge management. The objectives of the project are:

• to analyse and identify the characteristics of various types of organizational taxonomies and metadata used to organize and structure Websites, enterprise portals and institutional repositories
• to develop taxonomies for various applications, repositories and organizations.

We assume that the organization of a Website can be represented by taxonomy of concepts and terms, and that designing the information architecture of a Website involves a first step of constructing taxonomy as an abstract representation of the structure of the Website and organization of its contents. The taxonomy is then expressed as a navigation or search structure, manifested in one of many possible menu designs or interaction designs, and instantiated in a visual design (graphics design).

The project incorporates several small studies. One study involves surveying corporate Websites to identify common structures, facets, categories and terms used in organizing these Websites. This survey is limited to Websites of multinational companies that sell products.At the end of the survey, the taxonomy checklist will be improved in the light of the survey results, and can then be used as a reference by Information Architects when designing corporate Websites. Follow-up user studies can be carried out to investigate the effectiveness of the different facets in the taxonomy checklist, how they can be improved, how they should be used in the Website navigation system, and issues that designers should take into consideration.

Papers

• Wang, Z., Chaudhry, A.S., & Khoo, C. (in press). Using classification schemes and thesauri to build an organizational taxonomy for organizing content and aiding navigation. Journal of Documentation.
• Wang, Z., Chaudhry, A.S., & Khoo, C. (2006). Potential and prospects of taxonomies for content organization knowledge organization. Knowledge Organization, 33(3), 160-169.

Title of Project: Using Taxonomy of Learning Objects for Enhancing Knowledge Use and Reuse

Investigators: Abdus Sattar Chaudhry, Christopher Khoo, Yin Leng Theng, and Abdul Halim

Description:

This project investigates how taxonomy complemented by metadata designed to organize learning objects can facilitate knowledge use and reuse. The focus of the project is on learning objects that are to be used in the teaching and learning of knowledge management and information studies.

Papers

• Abdul Karim, A.H., Chaudhry, A.S., & Khoo, C. (2007). Learning objects application profile for granularity and reusability: Integrating Dublin Core with IEEE-LOM. In Proceedings of the International Conference on Dublin Core and Metadata Applications 2007 (DC 2007) (pp. 116-122). Singapore: Dublin Core Metadata Initiative and National Library Board Singapore.
• Khoo. C., Chaudhry, A.S., Theng, Y.L., & Abdul Karim, A.H. (2007). Potential for resource sharing of lecture materials using a repository of learning objects. Presented at the Seminar on Collaboration and Resource Sharing Among LIS schools, Mahasarakham University, Thailand, 17-19 August 2007.
• Chaudhry, A.S., Khoo, C., Theng, Y.-L., & Halim, A. (2006). Issues in developing a repository of learning objects for LIS education in Asia. In World Library and Information Congress: 72nd IFLA General Conference and Council. [Available: http://www.ifla.org/IV/ifla72/papers/107-Chaudhry_Khoo-en.pdf]
• Theng, Y.-L., Saputra, D., Goh, D., Foo, S., Chaudhry, A.S., Na, J.C., Khoo, C., Tan, M., Wu, P., Halim, A., & Lek, L. (2006). ReLOMS: Designing for effective use and reuse of learning objects for e-learning systems. In Proceedings of the Asia-Pacific Conference on Library & Information Education & Practice 2006 (A-LIEP 2006) (pp. 160-163). Singapore: School of Communication & Information, Nanyang Technological University.

Title of Project: How Users Organize Electronic Files on Their Workstations in the Office Environment

Investigators: Chris Khoo & Brendan Luyt

Description:

This is a study of how users organize electronic files on the harddisk of their office computers, the structural and labelling characteristics, file organization strategy and behavior, reasons and factors related to their behavior, and issues and problems encountered by users. The research questions that the project seeks to address fall into three areas:

• File structure: How are files organized into folders? What are the common types of folders and folder labels (folders are assumed to represent categories of files)? What are the common types of files and filenames? What are the common hierarchical structures? What are the temporal, spatial and organizational characteristics of the files and folders (for example, do some folders become obsolete and forgotten, are there duplicate folders, and so on)?
• User behaviour: How do users develop, maintain and manage their file structures? How do they locate and retrieve information and documents from their file structure? What problems do they encounter?
• User cognition and perception: What principles do users follow when organizing their files? What is their reason or rationale for organizing files and folders in a particular way? What perceptions do they have of their file structure and their behaviour?
• Relationships between file structure, user behaviour and cognition: How do user behaviour, cognition and perception affect file organization characteristics, and vice versa? File organization on the hard disk is related to personal information seeking behaviour—how people locate information in their own files and personal repositories.

The results will have implications for the design of file structures in operating systems, for designing personal information systems, for designing personal work spaces and personalization features in enterprise portals and organizational digital repositories. Further research in the future will build on this base to relate folder organization and naming conventions to particular groups of people and the occupational roles they engage in.

Papers

• Khoo, C., Luyt, B., Ee, C., Osman, J., Lim, H.H. & Yong, S. (In press). How users organize electronic files on their workstations in the office environment : A preliminary study of personal information seeking behaviour. To be presented at Information Seeking in Context (ISIC) Conference to be held at the University of Technology in Sydney, 19-21 July, 2006.

Title of Project: Human Categorization Behaviour

Investigators: Chris Khoo, Brendan Luyt

Description

This study seeks to find out how humans cluster Web pages naturally. Web search engines are developed to help users locate relevant Web pages, but they often retrieve too many pages. One promising approach to help users make sense of the large retrieval results and locate useful documents is to group the retrieved pages into clusters to give users an overview of the types of Web pages retrieved and allow users to select “promising” clusters for closer examination.

But what kind of clusters or categories are likely to be useful to the user and help the user locate relevant Web pages? Perhaps the useful categories are categories that the users themselves would use in grouping or clustering the Web pages. This study seeks to find out how human beings cluster Web pages. In particular, the study seeks to answer the following questions:

• What kind of categories are formed?
• How do people decide on the categories to use?
• How do they assign Web pages to the categories?
• What criteria are used in deciding on the categories and in the assignment of Web pages to categories?
• Are there “universal” or common categories that are created by many users?
• For the same set of Web pages and query, do different subjects form different categories? Are there differences between the categories constructed by subjects who contributed the query and subjects who did not contribute the query?
• What kind of features determine or explain the similarity of Web pages within each category?

Our expectation is that many of the categories formed will not be subject related categories but pertain to the form of the documents, the purpose of the author or the type of treatment given to the subject -- and other aspects that cut across subject categories. It is hoped that in the future, automatic methods can be developed to clustering or categorize Web pages in a way that mimics human clustering. Given the widespread evidence that humans adopt a “path of least cognitive resistance” approach to Web searching, reducing the cognitive load on users must be a prime concern of Web information retrieval. Our hypothesis is that if search results are organized in a way that is natural to human beings or is a reflection of how they might organize the results themselves, this will reduce the cognitive burden on users. The clusters will also help users gain an overall view and understanding of the different subsets of Web pages retrieved by their search.

Papers

• Khoo, C., Ng, K., & Ou, S. (2002). An exploratory study of human clustering of Web pages. In Lopez-Huertas, Maria J. (Ed.), Challenges in Knowledge Representation and Organization for the 21st century: Integration of Knowledge across Boundaries: Proceedings of the Seventh International ISKO Conference, Granada, Spain (pp. 351-357). (Advances in Knowledge Organization, Vol. 8). Germany: Ergon-Verlag.

Library & Information Science Education

Title of Project: Library & Information Science Education

Investigators: Chris Khoo, Abdus Sattar Chaudhry

Description

A series of studies on LIS education including:

• Survey of applicants to the MSc in Information Studies program
• Alumni survey
• Employer survey
• Analysis of job advertisemenst

Papers

• Chaudhry, A.S., Khoo, C., Wu, P., & Chang, Y.K. (2007). Trends in LIS education: Coverage of soft skills in curricula. In Proceeding of the Asia Pacific Conference on Library & Information Education & Practice 2007 (A-LIEP 2007), Taipei (pp. 1-14). Taipei: Dept. of Information & Communication, Shih-Hsin University.
• Chaudhry, A.S., & Khoo, C. (2007). Enhancing the quality of LIS education in Asia: Organizing teaching materials for sharing and reuse. In World Library and Information Congress: 73rd IFLA General Conference and Council. The Hague: International Federation of Library Associations and Institutions. Available: http://www.ifla.org/IV/ifla73/Programme2007.htm
• Foo, S., Khoo, C., Chaudhry, A.S., & Majid, S. (2006). Working together in developing library and information science education in the Asia Pacific. In 9th International Conference on Asian Digital Libraries, ICADL 2006: Proceedings (Lecture Notes in Computer Science, 4312/2006, pp. 4-11). Berlin: Springer.
• Khoo, C., Yeo, G., Yeung, C., Chandhry, A.S, Majid, M.S. (2006). A survey of non-traditional employment opportunities for information and knowledge professionals in Singapore. In Proceedings of the 13th Congress of Southeast Asian Librarians (CONSAL XIII), Manila.
• Khoo, C. (2005). Competencies for new era librarians and information professionals. Presented at the International Conference on Libraries (ICOL 2005), 14-16 March 2005, Penang, Malaysia.
• Khoo, C., & Chennupati, K.R. (2004). Profile of LIS applicants selecting different specialisations. Libri, 54(2), 67-81.
• Khoo, C., Higgins, S.E., Foo, S., & Lim, S.P. (2004). A cluster analysis of LIS students in Singapore and implications for defining areas of specialization. Journal of Education for Library and Information Science, 44(1), 36-57.
• Khoo, C., Majid, S., & Chaudhry, A.S. (2003). Developing an accreditation system for LIS professional education programmes in Southeast Asia: Issues and perspectives. Malaysian Journal of Library & Information Science, 8(2), 131-149.

Thesis Supervision

PhD & Research Master's Projects (Current)

Automatic Information Extraction and Text Mining in Medical Abstracts
Student: Wang Wei (M.A.Sc. student)
Supervisor: Christopher Khoo & Na Jin Cheon

Automatic Sentiment Analysis of News Articles
Student: Armineh Nourbaksh (M.A.Sc. student)
Supervisor: Christopher Khoo & Na Jin Cheon

Development of Taxonomies and Metadata Schemes to Support Reusable Learning Objects
Student: Abdul Halim Abdul Karim (PhD candidate)
Supervisor: Abdus Sattar Chaudhry & Christopher Khoo

Investigation of How Knowledge Organization Tools Can Be Used In the Development of Taxonomies for Organizational Repositories
Student: Wang Zhonghong (PhD candidate)
Supervisor: Abdus Sattar Chaudhry & Christopher Khoo

PhD (completed)

Automatic Multi-Document Summarization Using a Variable-Based Framework
Student: Ou Shiyan (PhD, June 2006)
Supervisor: Christopher Khoo & Dion Goh

Master's Theses (completed)

Developing a New Statistical Method for Chinese Text Segmentation
Student: Dai Yubin (M.A.Sc., 2000)
Supervisor: Christopher Khoo

Automatic Extraction of Cause-Effect Information from Medical Abstracts
Student: Niu Yun (M.A.Sc., 2000)
Supervisor: Christopher Khoo & Chan Syin

Combining Multiple Sources of Evidence for Information Retrieval
Student: Xi Wensi (M.A.Sc., 2000)
Supervisor: Christopher Khoo & Lim Ee Peng

Evaluation of Web-Based Online Catalogue Interfaces : A Cognitive Approach
Student: Cheng Lu (M.A.Sc., May 1999)
Supervisor: Christopher Khoo