TY - JOUR
T1 - Robust pseudo feedback estimation and HMM passage extraction
T2 - 15th Text REtrieval Conference, TREC 2006
AU - Jiang, Jing
AU - He, Xin
AU - Zhai, Cheng Xiang
PY - 2006
Y1 - 2006
N2 - In summary, in this year's Genomics Track, we focused on testing the effectiveness of two language modeling techniques for information retrieval on biomedical text. The general observation is that the two techniques are still effective to some degree on biomedical text. The regularized feedback estimation method is more robust than the original feedback estimation method because it needs less parameter tuning. The HMM-based passage extraction method can outperform paragraph-based passages. However, since the HMM-based method is not designed to extract short passages with very specific information, it needs some modification in order to fit this task. Finally, user relevance feedback is very effective.
AB - In summary, in this year's Genomics Track, we focused on testing the effectiveness of two language modeling techniques for information retrieval on biomedical text. The general observation is that the two techniques are still effective to some degree on biomedical text. The regularized feedback estimation method is more robust than the original feedback estimation method because it needs less parameter tuning. The HMM-based passage extraction method can outperform paragraph-based passages. However, since the HMM-based method is not designed to extract short passages with very specific information, it needs some modification in order to fit this task. Finally, user relevance feedback is very effective.
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M3 - Conference article
AN - SCOPUS:84873558377
SN - 1048-776X
JO - NIST Special Publication
JF - NIST Special Publication
Y2 - 14 November 2006 through 17 November 2006
ER -