About

LUPMIR integrated logical inference in language models for context-sensitive information retrieval.

This project, “Operationalizing the Logical Uncertainty Principle in a Language Modelling Framework for Context-based Information Retrieval (LUPMIR),” was funded by EPSRC (EP/E002145/1, GB 163K).

We aimed to investigate a solution to context-based information retrieval by integrating context-dependent logical inference into a statistical language modelling framework.

Publications

Lau, R., Li, Y., Song, D. and Kwok, R. (2008) Knowledge Discovery for Adaptive Negotiation Agents in e-Marketplaces, Decision Support Systems (unconditionally accepted for publication), Elsevier

Song, D., Huang, Q., Rueger, S. and Bruza, P. (2008) Facilitating Query Decomposition in Query Language Modeling by Association Rule Mining Using Multiple Sliding Windows, Accepted by the 30th European Conference on Information Retrieval (ECIR’2008), Glasgow, UK.

Huang, Q., Song, D. and Rueger, S. (2008) Robust Query-specific Pseudo Feedback Document Selection for Query Expansion, Accepted by the 30th European Conference on Information Retrieval (ECIR’2008), Glasgow, UK.

Huang, Q., Song, D., Rueger, S. and Bruza, P. (2007) Learning and Optimization of an Aspect Hidden Markov Model for Query Language Model Generation, The 1st International Conference on the Theory of Information Retrieval (ICTIR’2007), pp. 157-164.

Lau, R., Bruza, P. and Song, D. (2007) Towards a Belief Revision Based Adaptive and Context Sensitive Information Retrieval System, Accepted by ACM Transactions on Information Systems (TOIS)

Lau, R., Chung, A., Song, D. and Huang, Q. (2007) Towards Fuzzy Domain Ontology Based Concept Map Generation, The 6th International Conference on Web-based Learning.

Song, D., Lau, R., Bruza, P., Wong, K. and Chen, D. (2007) An Adaptive Information Agent for Document Title Classification and Filtering in Data Intensive Domains, Decision Support Systems, 44, pp. 251-265, Elsevier.

Song, D., Cao, G., Bruza, P. and Lau, R. (2007) Concept induction via fuzzy C-means clustering in a high-dimensional semantic space, in eds. J. Valente de Oliverira and W. Pedrycz, Advances in Fuzzy Clustering and its Applications, pp. 393-403, John Wiley & Sons