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Reducing Term Mismatch Probability by Exploiting Semantic Term Relations. (Réduire la probabilité de disparité des termes en exploitant leurs relations sémantiques)

Even though modern retrieval systems typically use a multitude of features to rank documents, the backbone for search ranking is usually the standard retrieval models.This thesis addresses a limitation of the standard retrieval models, the term mismatch problem. The term mismatch problem is a long standing problem in information retrieval. However, it was not well understood how often term mismatch happens in retrieval, how important it is for retrieval, or how it affects retrieval performance. This thesis answers the above questions.This research is enabled by the formal definition of term mismatch. In this thesis, term mismatch is defined as the probability that a term does not appear in a document given that this document is relevant. We propose several approaches for reducing term mismatch probability through modifying documents or queries. Our proposals are then followed by a quantitative analysis of term mismatch probability that shows how much the proposed approaches reduce term mismatch probability with maintaining the system performance. An essential component for achieving term mismatch probability reduction is the knowledge resource that defines terms and their relationships.First, we propose a document modification approach according to a user query. The main idea of our document modification approach is to deal with mismatched query terms. While prior research on document enrichment provides a static approach for document modification, we are concerned to only modify the document in case of mismatch. The modified document is then used in a standard retrieval model in order to obtain a mismatch aware retrieval model.Second, we propose a semantic query expansion approach based on a collaborative knowledge resource. We focus on the collaborative resource structure to obtain interesting expansion terms that contribute to reduce term mismatch probability, and as a result, improve the effectiveness of search.Third, we propose a query expansion approach based on neural language models. Neural language models are proposed to learn term vector representations, called distributed neural embeddings. Distributed neural embeddings capture relationships between terms, and they obtained impressive results comparing with state of the art approaches in term similarity tasks. However, in information retrieval, distributed neural embeddings are newly started to be exploited. We propose to use distributed neural embeddings as a knowledge resource in a query expansion scenario.Fourth, we apply the term mismatch probability definition for each contribution of the above contributions. We show how we use standard retrieval corpora with queries and relevance judgments to estimate the term mismatch probability. We estimate the term mismatch probability using original documents and queries, and we figure out how mismatch problem is clearly found in search systems for different types of indexing terms. Then, we point out how much our contributions reduce the estimated mismatch probability, and improve the system recall. As a result, we present how the modified document and query representations contribute to build a mismatch aware retrieval model that mitigate term mismatch problem theoretically and practically.This dissertation shows the effectiveness of our proposals to improve retrieval performance. Our experiments are conducted on corpora from two different domains: medical domain and cultural heritage domain. Moreover, we use two different types of indexing terms for representing documents and queries: words and concepts, and we exploit several types of relationships between indexing terms: hierarchical relationships, relationships based on a collaborative resource structure, relationships defined on distributed neural embeddings.Promising research directions are identified where the term mismatch research may make a significance impact on improving the search scenarios.