Foundations of statistical natural language processing
Foundations of statistical natural language processing
Exploring Requirements: Quality Before Design
Exploring Requirements: Quality Before Design
English as a Formal Specification Language
DEXA '02 Proceedings of the 13th International Workshop on Database and Expert Systems Applications
Introduction to the special issue on word sense disambiguation: the state of the art
Computational Linguistics - Special issue on word sense disambiguation
Verbs semantics and lexical selection
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Measuring the Expressiveness of a Constrained Natural Language: An Empirical Study
RE '05 Proceedings of the 13th IEEE International Conference on Requirements Engineering
WordNet: similarity - measuring the relatedness of concepts
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
SenseRelate targetword: a generalized framework for word sense disambiguation
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 4
A clustering-based approach for discovering flaws in requirements specifications
Proceedings of the 27th Annual ACM Symposium on Applied Computing
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Constrained Natural Languages (CNLs) are becoming an increasingly popular way of writing technical documents such as requirements specifications. This is because CNLs aim to reduce the ambiguity inherent within natural languages, whilst maintaining their readability and expressiveness. The design of existing CNLs appears to be unfocused towards achieving specific quality outcomes, in that the majority of lexical selections have been based upon lexicographer preferences rather than an optimum trade-off between quality factors such as ambiguity, readability, expressiveness, and lexical magnitude. In this paper we introduce the concept of 'replaceability' as a way of identifying the lexical redundancy inherent within a sample of requirements. Our novel and practical approach uses Natural Language Processing (NLP) techniques to enable us to make dynamic trade-offs between quality factors to optimise the resultant CNL. We also challenge the concept of a CNL being a one-dimensional static language, and demonstrate that our optimal-constraint process results in a CNL that can adapt to a changing domain while maintaining its expressiveness.