LUPMIR

Integrating 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)" is funded by EPSRC (EP/E002145/1, £163K). We aim to investigate a solution to context-based information retrieval, through integrating context-dependent logical inference in a statistical language modelling framework.

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)