TY - GEN
T1 - Utilizing focused relevance feedback
AU - Brondwine, Elinor
AU - Shtok, Anna
AU - Kurland, Oren
N1 - Publisher Copyright:
© 2016 ACM.
PY - 2016/7/7
Y1 - 2016/7/7
N2 - We present a novel study of ad hoc retrieval methods utilizing document-level relevance feedback and/or focused relevance feedback; namely, passages marked as (non-)relevant. The first method uses a novel mixture model that integrates relevant and non-relevant information at the language model level. The second method fuses retrieval scores produced by using relevant and non-relevant information separately. Empirical exploration attests to the merits of our methods, and sheds light on the effectiveness of using and integrating relevance feedback for textual units of varying granularities.
AB - We present a novel study of ad hoc retrieval methods utilizing document-level relevance feedback and/or focused relevance feedback; namely, passages marked as (non-)relevant. The first method uses a novel mixture model that integrates relevant and non-relevant information at the language model level. The second method fuses retrieval scores produced by using relevant and non-relevant information separately. Empirical exploration attests to the merits of our methods, and sheds light on the effectiveness of using and integrating relevance feedback for textual units of varying granularities.
KW - Focused relevance feedback
UR - http://www.scopus.com/inward/record.url?scp=84980361805&partnerID=8YFLogxK
U2 - 10.1145/2911451.2914695
DO - 10.1145/2911451.2914695
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AN - SCOPUS:84980361805
T3 - SIGIR 2016 - Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval
SP - 1061
EP - 1064
BT - SIGIR 2016 - Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval
T2 - 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2016
Y2 - 17 July 2016 through 21 July 2016
ER -