Using Term Location Information to Enhance Probabilistic Information Retrieval

Nouns are more important than other parts of speech in information retrieval and are more often found near the beginning or the end of sentences. In this paper, we investigate the effects of rewarding terms based on their location in sentences on information retrieval. Particularly, we propose a novel Term Location (TEL) retrieval model based on BM25 to enhance probabilistic information retrieval, where a kernel-based method is used to capture term placement patterns. Experiments on f ve TREC datasets of varied size and content indicate the proposed model signifcantly outperforms the optimized BM25 and DirichletLM in MAP over all datasets with all kernel functions, and excels the optimized BM25 and DirichletLM over most of the datasets in P@5 and P@20 with different kernel functions.