Utilizing term proximity for blog post retrieval

Term proximity is effective for many information retrieval (IR) research fields yet remains unexplored in blogosphere IR. The blogosphere is characterized by large amounts of noise, including incohesive, off-topic content and spam. Consequently, the classical bag-of-words unigram IR models are not reliable enough to provide robust and effective retrieval performance. In this article, we propose to boost the blog postretrieval performance by employing term proximity information. We investigate a variety of popular and state-of-the-art proximity-based statistical IR models, including a proximity-based counting model, the Markov random field (MRF) model, and the divergence from randomness (DFR) multinomial model. Extensive experimentation on the standard TREC Blog06 test dataset demonstrates that the introduction of term proximity information is indeed beneficial to retrieval from the blogosphere. Results also indicate the superiority of the unordered bi-gram model with the sequential-dependence phrases over other variants of the proximity-based models. Finally, inspired by the effectiveness of proximity models, we extend our study by exploring the proximity evidence between uery terms and opinionated terms. The consequent opinionated proximity model shows promising performance in the experiments.