Abstract
Requirement Analysis (RA) is a relevant application for Semantic Technologies focused on the extraction and exploitation of knowledge derived from technical documents. Language processing technologies are useful for the automatic extraction of concepts as well as norms (e.g. constraints on the use of devices) that play a key role in knowledge acquisition and design processes. A distributional method to train a kernel-based learning algorithm is here proposed, as a cost-effective approach for the validation stage in RA of Complex Systems, i.e. Naval Combat Systems. The targeted application of Requirement Identification and Information Extraction techniques is here discussed in the realm of robust search processes that allows to suitably locate software functionalities within large collections of requirements written in natural language.
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Keywords
- Natural Language Processing
- Semantic Similarity
- Information Extraction
- Requirement Analysis
- Mean Average Precision
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Garzoli, F., Croce, D., Nardini, M., Ciambra, F., Basili, R. (2013). Robust Requirements Analysis in Complex Systems through Machine Learning. In: Moschitti, A., Plank, B. (eds) Trustworthy Eternal Systems via Evolving Software, Data and Knowledge. EternalS 2012. Communications in Computer and Information Science, vol 379. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45260-4_4
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DOI: https://doi.org/10.1007/978-3-642-45260-4_4
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