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Improving the Robustness of Language Models - UIUC TREC 2003 Robust and Genomics Experiments

10 years 4 months ago
Improving the Robustness of Language Models - UIUC TREC 2003 Robust and Genomics Experiments
In this paper, we report our experiments in the TREC 2003 Genomics Track and the Robust Track. A common theme that we explored is the robustness of a basic language modeling retrieval approach. We examine several aspects of robustness, including robustness in handling different types of queries, different types of documents, and optimizing performance for difficult topics. Our basic retrieval method is the KL-divergence retrieval model with the two-stage smoothing method plus a mixture model feedback method. In the Genomics IR track, we propose a new method for modeling semi-structured queries using language models, which is shown to be more robust and effective than the regular query model in handling gene queries. In the Robust track, we experimented with two heuristic approaches to improve the robustness in using language models for pseudo feedback.
ChengXiang Zhai, Tao Tao, Hui Fang, Zhidi Shang
Added 01 Nov 2010
Updated 01 Nov 2010
Type Conference
Year 2003
Where TREC
Authors ChengXiang Zhai, Tao Tao, Hui Fang, Zhidi Shang
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