TY - GEN
T1 - A latent variable model for query expansion using the Hidden Markov Model
AU - Huang, Qiang
AU - Song, Dawei
PY - 2008
Y1 - 2008
N2 - We propose a novel probabilistic method based on the Hidden Markov Model (HMM) to learn the structure of a Latent Variable Model (LVM) for query language modeling. In the proposed LVM, the combinations of query terms are viewed as the latent variables and the segmented chunks from the feedback documents are used as the observations given these latent variables. Our extensive experiments shows that our method significantly outperforms a number of strong baselines in terms of both effectiveness and robustness.
AB - We propose a novel probabilistic method based on the Hidden Markov Model (HMM) to learn the structure of a Latent Variable Model (LVM) for query language modeling. In the proposed LVM, the combinations of query terms are viewed as the latent variables and the segmented chunks from the feedback documents are used as the observations given these latent variables. Our extensive experiments shows that our method significantly outperforms a number of strong baselines in terms of both effectiveness and robustness.
KW - Hidden markov model
KW - Information retrieval
KW - Latent variable model
UR - https://www.scopus.com/pages/publications/68249112982
U2 - 10.1145/1458082.1458310
DO - 10.1145/1458082.1458310
M3 - Conference contribution
AN - SCOPUS:68249112982
SN - 9781595939913
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 1417
EP - 1418
BT - Proceedings of the 17th ACM Conference on Information and Knowledge Management, CIKM'08
T2 - 17th ACM Conference on Information and Knowledge Management, CIKM'08
Y2 - 26 October 2008 through 30 October 2008
ER -