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Erschienen in:

01.06.2007

Linear feature-based models for information retrieval

verfasst von: Donald Metzler, W. Bruce Croft

Erschienen in: Discover Computing | Ausgabe 3/2007

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Abstract

There have been a number of linear, feature-based models proposed by the information retrieval community recently. Although each model is presented differently, they all share a common underlying framework. In this paper, we explore and discuss the theoretical issues of this framework, including a novel look at the parameter space. We then detail supervised training algorithms that directly maximize the evaluation metric under consideration, such as mean average precision. We present results that show training models in this way can lead to significantly better test set performance compared to other training methods that do not directly maximize the metric. Finally, we show that linear feature-based models can consistently and significantly outperform current state of the art retrieval models with the correct choice of features.

Vorheriger Artikel Learning-based summarisation of XML documents

Nächster Artikel Multilingual phrase-based concordance generation in real-time

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We assume ties are broken by document id.

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Titel: Linear feature-based models for information retrieval
verfasst von: Donald Metzler
W. Bruce Croft
Publikationsdatum: 01.06.2007
Verlag: Kluwer Academic Publishers
Erschienen in: Discover Computing / Ausgabe 3/2007
Print ISSN: 2948-2984
Elektronische ISSN: 2948-2992
DOI: https://doi.org/10.1007/s10791-006-9019-z

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