Anzeige
Erschienen in:
2016 | OriginalPaper | Buchkapitel
A Comparison of Deep Learning Based Query Expansion with Pseudo-Relevance Feedback and Mutual Information
verfasst von : Mohannad ALMasri, Catherine Berrut, Jean-Pierre Chevallet
Erschienen in: Advances in Information Retrieval
Verlag: Springer International Publishing
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Abstract
Automatic query expansion techniques are widely applied for improving text retrieval performance, using a variety of approaches that exploit several data sources for finding expansion terms. Selecting expansion terms is challenging and requires a framework capable of extracting term relationships. Recently, several Natural Language Processing methods, based on Deep Learning, are proposed for learning high quality vector representations of terms from a large amount of unstructured text with billions of words. These high quality vector representations capture a large number of term relationships. In this paper, we experimentally compare several expansion methods with expansion using these term vector representations. We use language models for information retrieval to evaluate expansion methods. Experiments conducted on four CLEF collections show a statistically significant improvement over the language models and other expansion models.