Logic-based information retrieval (IR) models represent the retrieval decision as an implication d→q between a document d and a query q, where d and q are logical sentences. However, d→q is generally a binary decision, thus we need a measurement to estimate the degree to which d implies q, denoted U(d→q)⁠. Most of the existing logic-based IR models either do not precisely define the implication d→q or use non-classical definitions. Some models also define the uncertainty U in informal ways. More importantly, they use two non-related frameworks to define d→q and its uncertainty U, even though the two notions are intrinsically related. The goal of this study is to propose a new logic-based IR model, which overcome these shortcomings. To this end, we first propose to replace the implication d→q by the validity of material implication⊨d⊃q⁠. Second, we redefine and adapt the mathematical relationship between logics, lattices and probability. Our new IR model presents a possible formalism for van Rijsbergen's intuition about replacing U(d→q) by P(q∣d)⁠.

Microblogging sites, like Twitter, have emerged as a popular platform for expressing opinions. Bound to 140 characters, Twitter's publications (tweets) are very short and not always written maintaining formal grammar and proper spelling. The spelling variations increase the likelihood of vocabulary mismatch and make the tweets difficult to understand without some kind of context. The task of tweet contextualization was organized around these issues. It aims to provide an automatic readable context explaining a given tweet, in order to help the reader understand this latter. In this paper, we describe statistical and semantic approaches for the tweet contextualization task. While the statistical one is based on association rules mining, the semantic one uses Wikipedia as an external knowledge source. The effectiveness of our approaches is proved through an experimental study conducted on the INEX 2013 collection.

Logic-based Information Retrieval (IR) models represent the retrieval decision as a logical implication d->q between a document d and a query q, where d and q are logical sentences. However, d->q is a binary decision, we thus need a measure to estimate the degree to which d implies q, denoted P(d->q). In this study, we revisit the Van Rijsbergen's assumptions about: 1- the logical implication ->' is not the material one, and 2- P(d->q) could be estimated by the conditional probability P(q|d). More precisely, we claim that the material implication is an appropriate implication for IR, and also we mathematically prove that replacing P(d->q) by P(q|d) is a correct choice. In order to prove the Van Rijsbergen's assumption, we use the Propositional Logic and the Lattice theory. We also exploit the notion of degree of implication that is proposed by Knuth.

With recommender systems, users receive items recommended on the basis of their profile. New users experience the cold start problem: as their profile is very poor, the system performs very poorly. In this paper, classical new user cold start techniques are improved by exploiting the cold user data, i.e. the user data that is readily available (e.g. age, occupation, location, etc.), in order to automatically associate the new user with a better first profile. Relying on the existing α-community spaces model, a rule-based induction process is used and a recommendation process based on the "level of agreement" principle is defined. The experiments show that the quality of recommendations compares to that obtained after a classical new user technique, while the new user effort is smaller as no initial ratings are asked.

This paper focuses on the RIME system aimed to the indexing of medical reports in a multimedia environment. This particular application is viewed as representative of a large set of still unanswered needs of large communities of users: domain experts dealing with on-line specialized documentation such as software engineers, medical specialists and so on. In this application textual information appears as an interesting media to access related pictures in the data base. After the presentation of the application and a study of the particular corpus involved we define a semantic model for the documents which is based on a Conceptual Language. Then we detail the indexing process and its various linguistic components which perform the translation of every medical report according to this semantic model.

This paper describes linguistic tools specifically designed for performant automatic indexing of natural language texts. By performant indexing, we mean the ability of the system to extract noun phrases (considered as main conceptual frames regarding text content) without processing full syntactic analysis of sentences (surface analyzer) both with its ability in learning unknown words. The paper describes the overall principles of this parser, emphasizing the use of syntactic networks and precedence matrix to fulfil the above goals of reducing the analysis cost and infering new vocabularies without interrupting the indexing process.