Combining Methods for the TREC 2003 Robust Track

Retrieval Track at this year’s conference. In the past we have investigated the use of alternate methods for tokenization and applied character n-grams, with success, to tasks in ad hoc, filtering, and crosslanguage tracks. For ranked retrieval, we have come to rely on a statistical language model to compute query/document similarity values. Hiemstra and de Vries describe such a linguistically motivated probabilistic model and explain how it relates to both the Boolean and vector space models [4]. The model has also been cast as a rudimentary Hidden Markov Model [7]. Although the model does not explicitly incorporate inverse document frequency, it does favor documents that contain more of the rare quer