Using terminology extraction techniques for improving traceability from formal models to textual requirements

cerbah2000aInternational audienceThis article deals with traceability in sotfware engineering. More precisely, we concentrate on the role of terminological knowledge the mapping between (informal) textual requirements and (formal) object models. We show that terminological knowledge facilitates production of traceability links, provided that language processing technologies allow to elaborate semi-automatically the required terminological resources. The presented system is one step towards incremental formalization from textual knowledge

Facilitating the development of controlled vocabularies for metabolomics technologies with text mining

BACKGROUND: Many bioinformatics applications rely on controlled vocabularies or ontologies to consistently interpret and seamlessly integrate information scattered across public resources. Experimental data sets from metabolomics studies need to be integrated with one another, but also with data produced by other types of omics studies in the spirit of systems biology, hence the pressing need for vocabularies and ontologies in metabolomics. However, it is time-consuming and non trivial to construct these resources manually. RESULTS: We describe a methodology for rapid development of controlled vocabularies, a study originally motivated by the needs for vocabularies describing metabolomics technologies. We present case studies involving two controlled vocabularies (for nuclear magnetic resonance spectroscopy and gas chromatography) whose development is currently underway as part of the Metabolomics Standards Initiative. The initial vocabularies were compiled manually, providing a total of 243 and 152 terms. A total of 5,699 and 2,612 new terms were acquired automatically from the literature. The analysis of the results showed that full-text articles (especially the Materials and Methods sections) are the major source of technology-specific terms as opposed to paper abstracts. CONCLUSIONS: We suggest a text mining method for efficient corpus-based term acquisition as a way of rapidly expanding a set of controlled vocabularies with the terms used in the scientific literature. We adopted an integrative approach, combining relatively generic software and data resources for time- and cost-effective development of a text mining tool for expansion of controlled vocabularies across various domains, as a practical alternative to both manual term collection and tailor-made named entity recognition methods

Compound Terms and Their Multi-word Variants: Case of German and Russian Languages

International audienceThe terminology of any language and any domain continuously evolves and leads to a constant term renewal. Terms undergo a wide range of morphological and syntactic variations which have to be handled by any NLP applications. If the syntactic variations of multi-word terms have been described and tools designed to process them, only a few works studied the syntagmatic variants of compound terms. This paper is dedicated to the identification of such variants, and more precisely to the detection of synonymic pairs that consist of “compound term - multi-word term”. We describe a pipeline for their detection, from compound recognition and splitting to alignment of the variants with original terms, through multi-word term extraction. The experiments are carried out for two compound-producing languages, German and Russian, and two specialised domains: wind energy and breast cancer. We identify variation patterns for these two languages and demonstrate that the transformation of a morphological compound into a syntagmatic compound mainly occurs when the term denomination needs to be enlarged