Abstract

Traditional information retrieval models treat the query as a bag of words, assuming that the occurrence of each query term is independent of the positions and occurrences of others. Several of these traditional models have been extended to incorporate positional information, most often through the inclusion of phrases. This has shown improvements in effectiveness on large, modern test collections. The language modeling approach to information retrieval is attractive because it provides a well-studied theoretical framework that has been successful in other fields. Incorporating positional information into language models is intuitive and has shown significant improvements in several language-modeling applications. However, attempts to integrate positional information into the language-modeling approach to IR have not shown consistent significant improvements. This paper provides a broader exploration of this problem. We apply the backoff technique to incorporate a bigram phrase language model with the traditional unigram one and compare its performance to an interpolation of a conditional bigram model with the unigram model. While this novel application of backoff does not improve effectiveness, we find that our formula for interpolating a conditional bigram model with the unigram model yields significantly different results from prior work. Namely, it shows an 11 % relative improvement in average precision on one query set, while yielding no improvement on the other two. 1.

Citations