Performance of Information Retrieval Models Using Term Co-occurrences

Many advanced models have been developed for information retrieval over the last years. These models are built on various artificial intelligence paradigms to improve the precision of the retrieval. Most of them exploit some form of term co-occurrences to improve retrieval quality. In this paper, we compare the retrieval performance of five of these models: the Extended Boolean model, the Generalized Vector Space model, the Frequent Set model, the Rough Set model and a Genetic-Based model. These models are tested on three sub-collections from TREC (Text REtrieval Conference). We analyze the specificity of the models regarding the form of co-occurrences introduced and report on the retrieval performance and the scalability of each model.