Learning Ranking vs. Modeling Relevance

D. Roussinov; Weiguo Fan

doi:10.1109/HICSS.2006.252

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Conference proceeding

Learning Ranking vs. Modeling Relevance

D. Roussinov and Weiguo Fan

Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS'06), Vol.3, pp.53a-53a

2006

DOI: 10.1109/HICSS.2006.252

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Abstract

The classical (ad hoc) document retrieval problem has been traditionally approached through ranking according to heuristically developed functions (such as tf.idf or bm25) or generative language modeling, which requires explicit assumptions about the term distributions. The nowadays popular discriminative (classification, machine learning, statistical forecasting etc.) approaches have been mostly abandoned while solving this task in spite of their success in a different task of text categorization. In this paper, we studied if a classifier can be trained solely based on labeled examples to successfully generalize to new (unseen by the system) queries and provide performance comparable with popular heuristic or language models. Our SVM-based classifier learns from the relevance judgments available with the standard test collections and generalizes to new, previously unseen queries its ability to compare and rank documents with respect to a given query. To accomplish this, we have designed a representation scheme, which is based on the discretized form of the high level statistics of the query term occurrences (such as tf, df, and document length) rather than individual terms. Using the standard metrics of average precision and the standard large and small test collections we confirmed that our machine learning approach can achieve the performance comparable with and better than the performance of the current state of the art models.

Appropriate technology

Information retrieval

Information systems

Machine learning

Management training

Space technology

Statistics

Support vector machines

Testing

Text categorization

Details

Title: Subtitle: Learning Ranking vs. Modeling Relevance
Creators: D. Roussinov - Arizona State University
Weiguo Fan - Virginia Tech
Resource Type: Conference proceeding
Publication Details: Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS'06), Vol.3, pp.53a-53a
Publisher: IEEE
DOI: 10.1109/HICSS.2006.252
ISSN: 1530-1605
eISSN: 2572-6862
Language: English
Date published: 2006
Academic Unit: Business Analytics
Record Identifier: 9984380550402771

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