Purpose. This work addresses an escalating problem within the realm of scientific publishing, that stems from accelerated publication rates of article formats difficult to process automatically. The amount of manual labour required to organise a comprehensive corpus of relevant literature has long been impractical. This has, in effect, reduced research efficiency and delayed scientific advancement. Two complementary approaches meant to alleviate this problem are detailed and improved upon beyond the current state-of-the-art, namely logical structure recovery of articles and keyphrase extraction. Methodology. The first approach targets the issue of flat-format publishing. It performs a structural analysis of the camera-ready PDF article and recognises its fine-grained organisation over logical units. The second approach is the application of a keyphrase extraction algorithm that relies on rhetorical information from the recovered structure to better contour an article’s true points of focus. A recount of the scientific article’s function, content and structure is provided, along with insights into how different logical components such as section headings or the bibliography can be automatically identified and utilised for higher-quality keyphrase extraction. Findings. Structure recovery can be carried out independently of an article’s formatting specifics, by exploiting conventional dependencies between logical components. In addition, access to an article’s logical structure is beneficial across term extraction approaches, reducing input noise and facilitating the emphasis of regions of interest. Value. The first part of this work details a novel method for recovering the rhetorical structure of scientific articles that is competitive with state-of-the-art machine learning techniques, yet requires no layout-specific tuning or prior training. The second part showcases a keyphrase extraction algorithm that outperforms other solutions in an established benchmark, yet does not rely on collection statistics or external knowledge sources in order to be proficient.

Thèse numérisée par la Division de la gestion de documents et des archives de l'Université de Montréal.

Thèse numérisée par la Division de la gestion de documents et des archives de l'Université de Montréal

For many years software engineering has primarily focused on the development of new systems and neglected maintenance and reengineering of legacy applications. Maintenance typically represents 70% of the cost during the life cycle of a system. In order to allow an efficient and safe maintenance of a legacy system, we need to reverse engineer it in order to reconstruct its missing or out-of-date documentation. In data-oriented applications the reverse engineering complexity can be broken down by considering that the database can be reverse engineered independently of the procedural components. Database reverse engineering can be defined as the process of recovering the database's schema(s) of an application from database declaration text and program source code that use the data in order to understand their exact structure and meaning. A database reverse engineering methodology is broken down into three processes: project preparation, data structure extraction that recovers the database's logical schema and data structure conceptualization that interprets the logical schema in conceptual terms. In order to validate our methodology and program understanding techniques, we have developed tools to support them. Those tools have proved absolutely necessary to perform database reverse engineering of medium to larger applications in reasonable time and at reasonable cost. To cut down on the cost of large projects, we have stressed the need for automation to reduce the manual work of the analyst. Our experience with real size projects has taught us that the management aspects of a project are essential success factors. The management of a project comprises different aspects such as database reverse engineering explanation, cost evaluation and database reverse engineering result evaluation.