Abstract

The critical attitude of many scientists and information professionals to the information published in the academic web, which is usually less valued than that available in scientific journals, is surprising. The article proposes that the Web should be taken seriously and techniques be developed for describing and evaluating the quality of web resources, which offer an opportunity for information professionals and users like students, scholars or researchers. Traditional document analysis is suggested, with the added value of describing the electronic address, size and format and using the possibilities of link analysis. Finally, the usefulness of the free bibliographic databases like Google Scholar and Scirus is discussed.

Abstract

This study proposes a pragmatic procedure of factual data analysis and retrieval of journalistic interviews and what we call “statement texts”. Journalistic documentation systems do not always offer the precision required to meet the specific demands of their end-users (journalists, historians, teachers, social scientists, general audience), as is the case with factual data. The retrieval of fact-based information is optimized by determing how users will ask for these data, simulating their pronominal searching behaviour, and then transforming those search modes into analytical categories for use by press documentalists. However, such studies should focus on a specific journalistic genre (in this case interviews and “statement texts”) because the conditions and structures of each genre or text type impose different analytical procedures and rules. Nonetheless, the resulting analogies and extrapolations should be carefully studied to improve the accuracy and relevance of the information system’s search results.

Abstract

ISBN: 978-1-4577-1350-7; International audience; This contest aims to provide a metric giving indications on the influence of individual document analysis stages to overall end-to-end applications. Contestants are provided with a full, working pipeline which operates on a page image to extract useful information. The pipeline is built with clearly identified analysis stages (e.g. binarization, skew detection, layout analysis, OCR ...) that have a formalized input and output. Contestants are invited to contribute their own algorithms as an alternative to one or more of the initially provided stages. The evaluation measures the overall impact of the contributed algorithm on the final (end-of-pipeline) output.