A Comparison of Deep Learning Based Query Expansion with Pseudo-Relevance Feedback and Mutual Information

Mohannad Almasri (1) , Catherine Berrut (1) , Jean-Pierre Chevallet (1)

1 MRIM - Modélisation et Recherche d’Information Multimédia [Grenoble]

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 large amounts of unstructured text data 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. The experiments are conducted on four CLEF collections show a statistically significant improvement over the language models and other expansion models.

Domaines: Recherche d'information [cs.IR]

Mohannad Almasri, Catherine Berrut, Jean-Pierre Chevallet. A Comparison of Deep Learning Based Query Expansion with Pseudo-Relevance Feedback and Mutual Information. Conférence ECIR, Mar 2016, Padoue, Italy. pp.369 - 715, ⟨10.1007/978-3-319-30671-1_57⟩. ⟨hal-01576603⟩