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LEAP4FNSSA lexicon: Towards a new dataset of keywords dealing with food security

The main objective of the project LEAP4FNSSA (Long-term EU-AU Research and Innovation Partnership for Food and Nutrition Security and Sustainable Agriculture) is to provide a tool for European and African institutions to engage in a sustainable partnership platform for research and innovation on Food and Nutrition Security, and Sustainable Agriculture (FNSSA). The FNSSA roadmap facilitates the involvement of stakeholders for addressing and linking research to innovation dealing with food security issues. In this context, the LEAP4FNSSA project supports the driving of the roadmap. Research and innovation activities were captured in different data, i.e. LEAP4FNSSA database and heterogeneous textual data including project reports, websites, scientific publications, workshop reports and student theses. The Knowledge Extractor Pipeline System (KEOPS) was implemented to support the processing and analysis of textual data associated with FNSSA activities. KEOPS is based on the LEAP4FNSSA lexicon presented in this data paper. The LEAP4FNSSA lexicon composed of 331 keywords associated with 12 concepts of the food security domain is the result of 3 steps of work and brainstorming. The lexicon enables the capturing of research and innovation topics dealing with food security and conducted by African and European partners. This data paper presents the obtained lexicon and a summary of the method to build it.

ITEXT-BIO: Intelligent Term EXTraction for BIOmedical analysis

Here, we introduce ITEXT-BIO, an intelligent process for biomedical domain terminology extraction from textual documents and subsequent analysis. The proposed methodology consists of two complementary approaches, including free and driven term extraction. The first is based on term extraction with statistical measures, while the second considers morphosyntactic variation rules to extract term variants from the corpus. The combination of two term extraction and analysis strategies is the keystone of ITEXT-BIO. These include combined intra-corpus strategies that enable term extraction and analysis either from a single corpus (intra), or from corpora (inter). We assessed the two approaches, the corpus or corpora to be analysed and the type of statistical measures used. Our experimental findings revealed that the proposed methodology could be used: (1) to efficiently extract representative, discriminant and new terms from a given corpus or corpora, and (2) to provide quantitative and qualitative analyses on these terms regarding the study domain.