A retrospective study of probabilistic context-based retrieval H. C. Wu, R. W. P. Luk, K. F. Wong, K. L. Kwok, and W. J. Lin   View Paper (PDF)   Return to Posters Abstract We propose a novel probabilistic retrieval model which weights terms according to their contexts in documents. The term weighting function of our model is similar to the language model and the binary independence model. The retrospective experiments (i.e., relevance information is present) illustrate the potential of our probabilistic context-based retrieval where the precision at the top 30 documents is about 43% for TREC-6 data and 52% for TREC-7 data. ©2006 Association for Computing Machinery