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DeNom: A Tool to Find Problematic Nominalizations using NLP

Landhäußer, Mathias 1; Körner, Sven J. 1; Keim, Jan 1; Tichy, Walter F. ORCID iD icon 1; Krisch, Jennifer
1 Karlsruher Institut für Technologie (KIT)


Nominalizations in natural language requirements specifications can lead to imprecision. For example, in the phrase "transportation of pallets" it is unclear who transports the pallets from where to where and how. Guidelines for requirements specifications therefore recommend avoiding nominalizations. However, not all nominalizations are problematic. We present an industrial-strength text analysis tool called DeNom, which detects problematic nominalizations and reports them to the user for reformulation.

DeNom uses Stanford's parser and the Cyc ontology. It classifies nominalizations as problematic or acceptable by first detecting all nominalizations in the specification and then subtracting those which are sufficiently specified within the sentence through word references, attributes, nominal phrase constructions, etc. All remaining nominalization are incompletely specified, and are therefore prone to conceal complex processes. These nominalizations are deemed problematic.

A thorough evaluation used 10 real-world requirements specifications from Daimler AG consisting of 60,000 words. DeNom identified over 1,100 nominalizations and classified 129 of them as problematic. ... mehr

DOI: 10.1109/AIRE.2015.7337623
Zitationen: 2
Zitationen: 2
Zugehörige Institution(en) am KIT Institut für Programmstrukturen und Datenorganisation (IPD)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2015
Sprache Englisch
Identifikator ISBN: 978-1-5090-0125-5
KITopen-ID: 1000051696
Erschienen in Second International Workshop on Artificial Intelligence for Requirements Engineering ( AIRE´15) : the 23rd IEEE International Requirements Engineering Conference (RE 2015) , August 24-28, 2015 Ottawa, Canada
Verlag Institute of Electrical and Electronics Engineers (IEEE)
Seiten 9-16
Nachgewiesen in Dimensions
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