Adaptive distributional extensions to DFR ranking

TY - GEN

T1 - Adaptive distributional extensions to DFR ranking

AU - Petersen, Casper

AU - Simonsen, Jakob Grue

AU - Järvelin, Kalervo

AU - Lioma, Christina

N1 - Conference code: 25

PY - 2016/10/24

Y1 - 2016/10/24

N2 - Divergence From Randomness (DFR) ranking models assume that informative terms are distributed in a corpus differently than non-informative terms. Different statistical models (e.g. Poisson, geometric) are used to model the distribution of non-informative terms, producing different DFR models. An informative term is then detected by measuring the divergence of its distribution from the distribution of non-informative terms. However, there is little empirical evidence that the distributions of non-informative terms used in DFR actually fit current datasets. Practically this risks providing a poor separation between informative and non-informative terms, thus compromising the discriminative power of the ranking model. We present a novel extension to DFR, which first detects the best-fitting distribution of non-informative terms in a collection, and then adapts the ranking computation to this best-fitting distribution. We call this model Adaptive Distributional Ranking (ADR) because it adapts the ranking to the statistics of the specific dataset being processed each time. Experiments on TREC data show ADR to outperform DFR models (and their extensions) and be comparable in performance to a query likelihood language model (LM).

AB - Divergence From Randomness (DFR) ranking models assume that informative terms are distributed in a corpus differently than non-informative terms. Different statistical models (e.g. Poisson, geometric) are used to model the distribution of non-informative terms, producing different DFR models. An informative term is then detected by measuring the divergence of its distribution from the distribution of non-informative terms. However, there is little empirical evidence that the distributions of non-informative terms used in DFR actually fit current datasets. Practically this risks providing a poor separation between informative and non-informative terms, thus compromising the discriminative power of the ranking model. We present a novel extension to DFR, which first detects the best-fitting distribution of non-informative terms in a collection, and then adapts the ranking computation to this best-fitting distribution. We call this model Adaptive Distributional Ranking (ADR) because it adapts the ranking to the statistics of the specific dataset being processed each time. Experiments on TREC data show ADR to outperform DFR models (and their extensions) and be comparable in performance to a query likelihood language model (LM).

KW - cs.IR

U2 - 10.1145/2983323.2983895

DO - 10.1145/2983323.2983895

M3 - Article in proceedings

SP - 2005

EP - 2008

BT - Proceedings of the 25th ACM International Conference on Information and Knowledge Management

PB - Association for Computing Machinery

T2 - 25th ACM International Conference on Information and Knowledge Management

Y2 - 24 October 2016 through 28 October 2016

ER -