2009Unpublished venueRequires access

A Study on Pseudo Labeled Document Constructed for Document Re-ranking

Chong Teng, Yanxiang He, Donghong Ji, Guimin Lin, Zhewei Mai

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Abstract

Document re-ranking is a middle module in information retrieval system. It's expected that more relevant documents with query appear in higher rankings, from which automatic query expansion can benefit, and it aims at improving the performance of the entire information retrieval. In this paper, we construct a pseudo labeled document based on pseudo-relevance feedback principle, and discuss about the relationship between performance of document re-ranking and the number of top documents in initial retrieval, the number of key terms from the top documents when constructing a pseudo labeled document. Experiment shows our approach of a pseudo labeled document constructed is greatly helpful to document re-ranking. It is the main contribution in the paper. Moreover, experiment shows the performance of document re-ranking is decreasing as the number of top documents increases; and increasing as the number of key terms from these documents increases.

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What this paper is about

Document re-ranking is a middle module in information retrieval system. It's expected that more relevant documents with query appear in higher rankings, from which automatic query expansion can benefit, and it aims at improving the performance of the entire information retrieval. In this paper, we construct a pseudo labeled document based on pseudo-relevance feedback principle, and discuss about the relationship between performance of document re-ranking and the number of top documents in initial retrieval, the number of key terms from the top documents when constructing a pseudo labeled document. Experiment shows our approach of a pseudo labeled document constructed is greatly helpful to document re-ranking. It is the main contribution in the paper. Moreover, experiment shows the performance of document re-ranking is decreasing as the number of top documents increases; and increasing as the number of key terms from these documents increases.

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Available abstract

Document re-ranking is a middle module in information retrieval system. It's expected that more relevant documents with query appear in higher rankings, from which automatic query expansion can benefit, and it aims at improving the performance of the entire information retrieval. In this paper, we construct a pseudo labeled document based on pseudo-relevance feedback principle, and discuss about the relationship between performance of document re-ranking and the number of top documents in initial retrieval, the number of key terms from the top documents when constructing a pseudo labeled document. Experiment shows our approach of a pseudo labeled document constructed is greatly helpful to document re-ranking. It is the main contribution in the paper. Moreover, experiment shows the performance of document re-ranking is decreasing as the number of top documents increases; and increasing as the number of key terms from these documents increases.

Key concepts: Ranking (information retrieval), Information retrieval, Computer science, Relevance (law), Document retrieval, Key (lock), Construct (python library), Query expansion

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