Passage Retrieval in Log Files: An Approach Based on Query Enrichment

• Main Authors: Saneifar, Hassan, Bonniol, Stéphane, Laurent, Anne, Poncelet, Pascal, Roche, Mathieu
• Other Authors: Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Satin IP Technologies, Université Montpellier 2 - Sciences et Techniques (UM2), Fouille de données environnementales (TATOO), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Exploration et exploitation de données textuelles (TEXTE), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre national du machinisme agricole, du génie rural, des eaux et forêts (CEMAGREF)
• Format: Conference Object
• Language: English
• Published: HAL CCSD 2010
• Subjects: [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB]; [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR]; Iceland
• Online Access: https://hal-lirmm.ccsd.cnrs.fr/lirmm-00816291; https://hal-lirmm.ccsd.cnrs.fr/lirmm-00816291/document; https://hal-lirmm.ccsd.cnrs.fr/lirmm-00816291/file/IceTAL2010.pdf; https://doi.org/10.1007/978-3-642-14770-8_39

International audience The question answering systems are considered the next generation of search engines. This paper focuses on the first step of this process, which is to search for relevant passages containing answers. Passage Retrieval, can be difficult because of the complexity of data, log files in our case. Our contribution is based on the enrichment of queries by using a learning method and a novel term weighting function. This original term weighting function, used within the enrichment process, aims to assign a weight to terms according to their relatedness to the context of answers. Experiments conducted on real data show that our protocol of primitive query enrichment make it possible to retrieve relevant passages.