BioEve Search: A Novel Framework to Facilitate Interactive Literature Search

Background. Recent advances in computational and biological methods in last two decades have remarkably changed the scale of biomedical research and with it began the unprecedented growth in both the production of biomedical data and amount of published literature discussing it. An automated extraction system coupled with a cognitive search and navigation service over these document collections would not only save time and effort, but also pave the way to discover hitherto unknown information implicitly conveyed in the texts. Results. We developed a novel framework (named “BioEve”) that seamlessly integrates Faceted Search (Information Retrieval) with Information Extraction module to provide an interactive search experience for the researchers in life sciences. It enables guided step-by-step search query refinement, by suggesting concepts and entities (like genes, drugs, and diseases) to quickly filter and modify search direction, and thereby facilitating an enriched paradigm where user can discover related concepts and keywords to search while information seeking. Conclusions. The BioEve Search framework makes it easier to enable scalable interactive search over large collection of textual articles and to discover knowledge hidden in thousands of biomedical literature articles with ease

Filtrage automatique de courriels : une approche adaptative et multi niveaux

International audienceCet article propose un système de courriers électroniques paramétrable avec plusieurs niveaux de filtrage: un filtrage simple basé sur l'information contenue dans l'entête du courriel ; un filtrage booléen basé sur l'existence ou non de mots clés dans le corps du courriel ; un filtrage vectoriel basé sur le poids de contribution des mots clés du courriel ; un filtrage approfondi basé sur les propriétés linguistiques caractérisant la structure et le contenu du courriel. Nous proposons une solution adaptative qui offre au système la possibilité d'apprendre à partir de données, de modifier ses connaissances et de s'adapter à l'évolution des intérêts de l'utilisateur et à la variation de la nature des courriels dans le temps. De plus, nous utilisons un réseau lexical permettant d'améliorer la représentation du courriel en prenant en considération l'aspect sémantique.<BR /

Text Categorization and Sorting of Web Search Results

With the Internet facing the growing problem of information overload, the large volumes, weak structure and noisiness of Web data make it amenable to the application of machine learning techniques. After providing an overview of several topics in text categorization, including document representation, feature selection, and a choice of classifiers, the paper presents experimental results concerning the performance and effects of different transformations of the bag-of-words document representation and feature selection, on texts extracted from the dmoz Open Directory of Web pages. Finally, the paper describes the primary motivation for the experiments: a new meta-search engine CatS which utilizes text categorization to enhance the presentation of search results obtained from a major Web search engine

Training and assessing classification rules with unbalanced data

The problem of modeling binary responses by using cross-sectional data has been addressed with a number of satisfying solutions that draw on both parametric and nonparametric methods. However, there exist many real situations where one of the two responses (usually the most interesting for the analysis) is rare. It has been largely reported that this class imbalance heavily compromises the process of learning, because the model tends to focus on the prevalent class and to ignore the rare events. However, not only the estimation of the classification model is affected by a skewed distribution of the classes, but also the evaluation of its accuracy is jeopardized, because the scarcity of data leads to poor estimates of the model’s accuracy. In this work, the effects of class imbalance on model training and model assessing are discussed. Moreover, a unified and systematic framework for dealing with both the problems is proposed, based on a smoothed bootstrap re-sampling technique