Discretization based learning approach to information retrieval

We approached the problem as learning how to order documents by estimated relevance with respect to a user query. Our support vector machines based classifier learns from the relevance judgments available with the standard test collections and generalizes to new, previously unseen queries. For this, we have designed a representation scheme, which is based on the discrete representation of the local (lw) and global (gw) weighting functions, thus is capable of reproducing and enhancing the properties of such popular ranking functions as tf.idf, BM25 or those based on language models. Our tests with the standard test collections have demonstrated the capability of our approach to achieve the performance of the best known scoring functions solely from the labeled examples and without taking advantage of knowing those functions or their important properties or parameters.

[1]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[2]  Chris Buckley,et al.  A probabilistic learning approach for document indexing , 1991, TOIS.

[3]  John D. Lafferty,et al.  A study of smoothing methods for language models applied to Ad Hoc information retrieval , 2001, SIGIR '01.

[4]  Garrison W. Cottrell,et al.  Fusion Via a Linear Combination of Scores , 1999, Information Retrieval.

[5]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[6]  Ron Kohavi,et al.  Supervised and Unsupervised Discretization of Continuous Features , 1995, ICML.

[7]  Fredric C. Gey,et al.  Inferring probability of relevance using the method of logistic regression , 1994, SIGIR '94.

[8]  Edward A. Fox,et al.  Tuning before feedback: combining ranking discovery and blind feedback for robust retrieval , 2004, SIGIR '04.

[9]  Garrison W. Cottrell,et al.  Optimizing parameters in a ranked retrieval system using multi-query relevance feedback , 1994 .

[10]  Yoram Singer,et al.  Learning to Order Things , 1997, NIPS.

[11]  Marti A. Hearst.  Trends & Controversies: Support Vector Machines , 1998, IEEE Intell. Syst..

[12]  Chun-Nan Hsu,et al.  Why Discretization Works for Naive Bayesian Classifiers , 2000, ICML.

[13]  Stephen E. Robertson,et al.  Okapi at TREC-3 , 1994, TREC.

[14]  W. Bruce Croft,et al.  A general language model for information retrieval , 1999, CIKM '99.

[15]  Ramesh Nallapati,et al.  Discriminative models for information retrieval , 2004, SIGIR '04.

[16]  Thorsten Joachims,et al.  Optimizing search engines using clickthrough data , 2002, KDD.

[17]  Stephen E. Robertson,et al.  Relevance weighting of search terms , 1976, J. Am. Soc. Inf. Sci..

[18]  Thorsten Joachims,et al.  A statistical learning learning model of text classification for support vector machines , 2001, SIGIR '01.

[19]  Michael McGill,et al.  Introduction to Modern Information Retrieval , 1983 .