Hundreds of millions of users each day search the web and other repositories to meet their information needs. However, queries can fail to find documents due to a mismatch in terminology. Query expansion seeks to address this problem by automatically adding terms from highly ranked documents to the query. While query expansion has been shown to be effective at improving query performance, the gain in effectiveness comes at a cost: expansion is slow and resource-intensive. Current techniques for query expansion use fixed values for key parameters, determined by tuning on test collections. We show that these parameters may not be generally applicable, and, more significantly, that the assumption that the same parameter settings can be used for all queries is invalid. Using detailed experiments, we demonstrate that new methods for choosing parameters must be found. In conventional approaches to query expansion, the additional terms are selected from highly ranked documents returned from an initial retrieval run. We demonstrate a new method of obtaining expansion terms, based on past user queries that are associated with documents in the collection. The most effective query expansion methods rely on costly retrieval and processing of feedback documents. We explore alternative methods for reducing query-evaluation costs, and propose a new method based on keeping a brief summary of each document in memory. This method allows query expansion to proceed three times faster than previously, while approximating the effectiveness of standard expansion. We investigate the use of document expansion, in which documents are augmented with related terms extracted from the corpus during indexing, as an alternative to query expansion. The overheads at query time are small. We propose and explore a range of corpus-based document expansion techniques and compare them to corpus-based query expansion on TREC data. These experiments show that document expansion delivers at best limited benefits, while query expansion � including standard techniques and efficient approaches described in recent work � usually delivers good gains. We conclude that document expansion is unpromising, but it is likely that the efficiency of query expansion can be further improved.

Hundreds of millions of users each day search the web and other repositories to meet their information needs. However, queries can fail to find documents due to a mismatch in terminology. Query expansion seeks to address this problem by automatically adding terms from highly ranked documents to the query. While query expansion has been shown to be effective at improving query performance, the gain in effectiveness comes at a cost: expansion is slow and resource-intensive. Current techniques for query expansion use fixed values for key parameters, determined by tuning on test collections. We show that these parameters may not be generally applicable, and, more significantly, that the assumption that the same parameter settings can be used for all queries is invalid. Using detailed experiments, we demonstrate that new methods for choosing parameters must be found. In conventional approaches to query expansion, the additional terms are selected from highly ranked do cuments returned from an initial retrieval run. We demonstrate a new method of obtaining expansion terms, based on past user queries that are associated with documents in the collection. The most effective query expansion methods rely on costly retrieval and processing of feedback documents. We explore alternative methods for reducing query-evaluation costs, and propose a new method based on keeping a brief summary of each document in memory. This method allows query expansion to proceed three times faster than previously, while approximating the effectiveness of standard expansion. We investigate the use of document expansion, in which documents are augmented with related terms extracted from the corpus during indexing, as an alternative to query expansion. The overheads at query time are small. We propose and explore a range of corpus-based document expansion techniques and compare them to corpus-based query expansion on TREC data. These experiments show that document expansion delivers at best limited ben efits, while query expansion, including standard techniques and efficient approaches described in recent work, usually delivers good gains. We conclude that document expansion is unpromising, but it is likely that the efficiency of query expansion can be further improved.

Quand des humains cherchent des informations au sein de bases de connaissancesou de collections de documents, ils utilisent un système de recherche d’information(SRI) faisant office d’interface. Les utilisateurs doivent alors transmettre au SRI unereprésentation de leur besoin d’information afin que celui-ci puisse chercher des documentscontenant des informations pertinentes. De nos jours, la représentation du besoind’information est constituée d’un petit ensemble de mots-clés plus souvent connu sousla dénomination de « requête ». Or, quelques mots peuvent ne pas être suffisants pourreprésenter précisément et efficacement l’état cognitif complet d’un humain par rapportà son besoin d’information initial. Sans une certaine forme de contexte thématiquecomplémentaire, le SRI peut ne pas renvoyer certains documents pertinents exprimantdes concepts n’étant pas explicitement évoqués dans la requête.Dans cette thèse, nous explorons et proposons différentes méthodes statistiques, automatiqueset non supervisées pour la représentation du contexte thématique de larequête. Plus spécifiquement, nous cherchons à identifier les différents concepts implicitesd’une requête formulée par un utilisateur sans qu’aucune action de sa part nesoit nécessaire. Nous expérimentons pour cela l’utilisation et la combinaison de différentessources d’information générales représentant les grands types d’informationauxquels nous sommes confrontés quotidiennement sur internet. Nous tirons égalementparti d’algorithmes de modélisation thématique probabiliste (tels que l’allocationde Dirichlet latente) dans le cadre d’un retour de pertinence simulé. Nous proposonspar ailleurs une méthode permettant d’estimer conjointement le nombre de conceptsimplicites d’une requête ainsi que l’ensemble de documents pseudo-pertinent le plusapproprié afin de modéliser ces concepts. Nous évaluons nos approches en utilisantquatre collections de test TREC de grande taille. En annexes, nous proposons égalementune approche de contextualisation de messages courts exploitant des méthodesde recherche d’information et de résumé automatique / When searching for information within knowledge bases or document collections,humans use an information retrieval system (IRS). So that it can retrieve documentscontaining relevant information, users have to provide the IRS with a representationof their information need. Nowadays, this representation of the information need iscomposed of a small set of keywords often referred to as the « query ». A few wordsmay however not be sufficient to accurately and effectively represent the complete cognitivestate of a human with respect to her initial information need. A query may notcontain sufficient information if the user is searching for some topic in which she is notconfident at all. Hence, without some kind of context, the IRS could simply miss somenuances or details that the user did not – or could not – provide in query.In this thesis, we explore and propose various statistic, automatic and unsupervisedmethods for representing the topical context of the query. More specifically, we aim toidentify the latent concepts of a query without involving the user in the process norrequiring explicit feedback. We experiment using and combining several general informationsources representing the main types of information we deal with on a dailybasis while browsing theWeb.We also leverage probabilistic topic models (such as LatentDirichlet Allocation) in a pseudo-relevance feedback setting. Besides, we proposea method allowing to jointly estimate the number of latent concepts of a query andthe set of pseudo-relevant feedback documents which is the most suitable to modelthese concepts. We evaluate our approaches using four main large TREC test collections.In the appendix of this thesis, we also propose an approach for contextualizingshort messages which leverages both information retrieval and automatic summarizationtechniques