INTSORMIL 2002 Annual Report

The global community confronts an enonnous task of stimulating economic growth in rural areas where 75% of the very poor (90% in Africa) currently live and ensuring the nutritional security of a world popUlation that is growing in size and evolving in consumption patterns without intensifying environmental degradation, social security, or adverse consequences for human health. This challenge is not only great but it is also urgent. Today, access to food, sufficient, safe, and nutritious food, is the primary problem for nearly 800 million chronically undernourished people. Unless we act now, the next few decades will almost certainly find us unable to produce agricultural products sufficient to meet the demands of growing populations and changing diets. The majority of poor live in rural areas in developing countries and agricultural and food systems development is vital to economic growth; improving environmental quality; strengthening nutrition, health and child survival; improving the status of women; and promoting democratization. Over the next 50 years, the global population will increase to 8-10 billion, requiring advances in scientific knowledge across a broad range of agricultural endeavors, i.e., developing more productive food and commodity cultivars, improving nutritional quality of crop and livestock products, reducing food and commodity yield losses due to pests and diseases, ensuring healthy livestock, developing sustainable and responsible fisheries and aquaculture practices, optimizing the use of forests, managing water more efficiently, protecting and improving land productivity, and conserving and managing genetic diversity

Unsupervised Natural Language Processing for Knowledge Extraction from Domain-specific Textual Resources

This thesis aims to develop a Relation Extraction algorithm to extract knowledge out of automotive data. While most approaches to Relation Extraction are only evaluated on newspaper data dealing with general relations from the business world their applicability to other data sets is not well studied. Part I of this thesis deals with theoretical foundations of Information Extraction algorithms. Text mining cannot be seen as the simple application of data mining methods to textual data. Instead, sophisticated methods have to be employed to accurately extract knowledge from text which then can be mined using statistical methods from the field of data mining. Information Extraction itself can be divided into two subtasks: Entity Detection and Relation Extraction. The detection of entities is very domain-dependent due to terminology, abbreviations and general language use within the given domain. Thus, this task has to be solved for each domain employing thesauri or another type of lexicon. Supervised approaches to Named Entity Recognition will not achieve reasonable results unless they have been trained for the given type of data. The task of Relation Extraction can be basically approached by pattern-based and kernel-based algorithms. The latter achieve state-of-the-art results on newspaper data and point out the importance of linguistic features. In order to analyze relations contained in textual data, syntactic features like part-of-speech tags and syntactic parses are essential. Chapter 4 presents machine learning approaches and linguistic foundations being essential for syntactic annotation of textual data and Relation Extraction. Chapter 6 analyzes the performance of state-of-the-art algorithms of POS tagging, syntactic parsing and Relation Extraction on automotive data. The findings are: supervised methods trained on newspaper corpora do not achieve accurate results when being applied on automotive data. This is grounded in various reasons. Besides low-quality text, the nature of automotive relations states the main challenge. Automotive relation types of interest (e. g. component – symptom) are rather arbitrary compared to well-studied relation types like is-a or is-head-of. In order to achieve acceptable results, algorithms have to be trained directly on this kind of data. As the manual annotation of data for each language and data type is too costly and inflexible, unsupervised methods are the ones to rely on. Part II deals with the development of dedicated algorithms for all three essential tasks. Unsupervised POS tagging (Chapter 7) is a well-studied task and algorithms achieving accurate tagging exist. All of them do not disambiguate high frequency words, only out-of-lexicon words are disambiguated. Most high frequency words bear syntactic information and thus, it is very important to differentiate between their different functions. Especially domain languages contain ambiguous and high frequent words bearing semantic information (e. g. pump). In order to improve POS tagging, an algorithm for disambiguation is developed and used to enhance an existing state-of-the-art tagger. This approach is based on context clustering which is used to detect a word type’s different syntactic functions. Evaluation shows that tagging accuracy is raised significantly. An approach to unsupervised syntactic parsing (Chapter 8) is developed in order to suffice the requirements of Relation Extraction. These requirements include high precision results on nominal and prepositional phrases as they contain the entities being relevant for Relation Extraction. Furthermore, accurate shallow parsing is more desirable than deep binary parsing as it facilitates Relation Extraction more than deep parsing. Endocentric and exocentric constructions can be distinguished and improve proper phrase labeling. unsuParse is based on preferred positions of word types within phrases to detect phrase candidates. Iterating the detection of simple phrases successively induces deeper structures. The proposed algorithm fulfills all demanded criteria and achieves competitive results on standard evaluation setups. Syntactic Relation Extraction (Chapter 9) is an approach exploiting syntactic statistics and text characteristics to extract relations between previously annotated entities. The approach is based on entity distributions given in a corpus and thus, provides a possibility to extend text mining processes to new data in an unsupervised manner. Evaluation on two different languages and two different text types of the automotive domain shows that it achieves accurate results on repair order data. Results are less accurate on internet data, but the task of sentiment analysis and extraction of the opinion target can be mastered. Thus, the incorporation of internet data is possible and important as it provides useful insight into the customer\''s thoughts. To conclude, this thesis presents a complete unsupervised workflow for Relation Extraction – except for the highly domain-dependent Entity Detection task – improving performance of each of the involved subtasks compared to state-of-the-art approaches. Furthermore, this work applies Natural Language Processing methods and Relation Extraction approaches to real world data unveiling challenges that do not occur in high quality newspaper corpora

Book of abstracts, 4th World Congress on Agroforestry

International audienc

BEYOND ALL LIMITS : Procedings on International Conference on Sustainability in Architecture, Planning, and Design : 11-12, 13 May 2022

[Italiano]: Il volume raccoglie gli atti della seconda edizione del convegno “BEYOND ALL LIMITS. International Conference on Sustainability in Architecture, Planning, and Design”, tenutosi nei giorni 11 e 12 maggio 2022, presso il Complesso del Belvedere di San Leucio, sede di Officina Vanvitelli. Il convegno è stato promosso e organizzato dal Dipartimento di Architettura e Disegno Industriale dell'Università degli Studi della Campania “Luigi Vanvitelli”, in partnership con la Faculty of Architecture della Çankaya University di Ankara e la Faculty of Engineering della University of Strathclyde di Glasgow. L’obiettivo principale di questo convegno scientifico e multidisciplinare, che ha interessato i campi dell'architettura, della pianificazione e del design, è stato quello di affrontare il tema della sostenibilità all’interno dell'attuale dibattito internazionale scaturito dal New European Bauhaus (NEB)./[English]: This volume collects the Proceedings of the second edition of the conference “BEYOND ALL LIMITS. International Conference on Sustainability in Architecture, Planning, and Design”, held on May 11 and 12, 2022, at the San Leucio Belvedere Complex, home of Officina Vanvitelli. The conference was sponsored and organized by the Department of Architecture and Industrial Design of the University of Campania “Luigi Vanvitelli”, in partnership with the Faculty of Architecture of Çankaya University in Ankara and the Faculty of Engineering of the University of Strathclyde in Glasgow. The main objective of this scientific and multidisciplinary conference, which covered the fields of architecture, planning and design, was to address the issue of sustainability within the current international debate that has arisen from the New European Bauhaus (NEB)

Third International Symposium on Space Mission Operations and Ground Data Systems, part 1

Under the theme of 'Opportunities in Ground Data Systems for High Efficiency Operations of Space Missions,' the SpaceOps '94 symposium included presentations of more than 150 technical papers spanning five topic areas: Mission Management, Operations, Data Management, System Development, and Systems Engineering. The papers focus on improvements in the efficiency, effectiveness, productivity, and quality of data acquisition, ground systems, and mission operations. New technology, techniques, methods, and human systems are discussed. Accomplishments are also reported in the application of information systems to improve data retrieval, reporting, and archiving; the management of human factors; the use of telescience and teleoperations; and the design and implementation of logistics support for mission operations