Embedding Web-based Statistical Translation Models in Cross-Language Information Retrieval

Although more and more language pairs are covered by machine translation services, there are still many pairs that lack translation resources. Cross-language information retrieval (CLIR) is an application which needs translation functionality of a relatively low level of sophistication since current models for information retrieval (IR) are still based on a bag-of-words. The Web provides a vast resource for the automatic construction of parallel corpora which can be used to train statistical translation models automatically. The resulting translation models can be embedded in several ways in a retrieval model. In this paper, we will investigate the problem of automatically mining parallel texts from the Web and different ways of integrating the translation models within the retrieval process. Our experiments on standard test collections for CLIR show that the Web-based translation models can surpass commercial MT systems in CLIR tasks. These results open the perspective of constructing a fully automatic query translation device for CLIR at a very low cost.Comment: 37 page

Intégration des Analyses du Français dans la Recherche d'Information

International audienceCet article décrit des approches que nous avons implantées dans le cadre d'une collaboration de recherche entre nos deux groupes. Ces approches visent à créer une représentation plus précise pour les documents et les requêtes dans un SRI. Elles sont basées sur des extractions de termes composés, au lieu de termes simples utilisés dans les approches traditionnelles. Deux approches sont employées: par une analyse syntaxico-statistique et par l'utilisation d'une base de terminologie manuelle. Nous décrivons ces deux approches, ainsi que les résultats préliminaires obtenus

Using a Medical Thesaurus to Predict Query Difficulty

International audienceEstimating query performance is the task of predicting the quality of results returned by a search engine in response to a query. In this paper, we focus on pre-retrieval prediction methods for the medical domain. We propose a novel predictor that exploits a thesaurus to as- certain how difficult queries are. In our experiments, we show that our predictor outperforms the state-of-the-art methods that do not use a thesaurus

Augmenting Ad-Hoc IR Dataset for Interactive Conversational Search

A peculiarity of conversational search systems is that they involve mixed-initiatives such as system-generated query clarifying questions. Evaluating those systems at a large scale on the end task of IR is very challenging, requiring adequate datasets containing such interactions. However, current datasets only focus on either traditional ad-hoc IR tasks or query clarification tasks, the latter being usually seen as a reformulation task from the initial query. The only two datasets known to us that contain both document relevance judgments and the associated clarification interactions are Qulac and ClariQ. Both are based on the TREC Web Track 2009-12 collection, but cover a very limited number of topics (237 topics), far from being enough for training and testing conversational IR models. To fill the gap, we propose a methodology to automatically build large-scale conversational IR datasets from ad-hoc IR datasets in order to facilitate explorations on conversational IR. Our methodology is based on two processes: 1) generating query clarification interactions through query clarification and answer generators, and 2) augmenting ad-hoc IR datasets with simulated interactions. In this paper, we focus on MsMarco and augment it with query clarification and answer simulations. We perform a thorough evaluation showing the quality and the relevance of the generated interactions for each initial query. This paper shows the feasibility and utility of augmenting ad-hoc IR datasets for conversational IR