Latent concept expansion using markov random fields
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
| Hi-index | 0.00 |
We investigate the use of multi-term query concepts to improve the performance of text-retrieval systems that accept ``natural-language'''' queries. A relevance feedback process is explained that massively expands an initial query with single and multi-term concepts. The multi-term concepts are modelled as a set of words appearing within windows of varying sizes. Experimental results suggest that windows of larger size yield improvements in average precision. The reason for this improvement is explored. A window size relaxation process that yields a significant reduction in expanded query size with no performance loss is also described.