Automated analysis of requirement specifications
ICSE '97 Proceedings of the 19th international conference on Software engineering
Bracketing Compound Nouns for Logic Form Derivation
Proceedings of the Fifteenth International Florida Artificial Intelligence Research Society Conference
Processing natural language requirements
ASE '97 Proceedings of the 12th international conference on Automated software engineering (formerly: KBSE)
SEW '01 Proceedings of the 26th Annual NASA Goddard Software Engineering Workshop
Symmetric pattern matching analysis for English coordinate structures
ANLC '94 Proceedings of the fourth conference on Applied natural language processing
A simple but useful approach to conjunct identification
ACL '92 Proceedings of the 30th annual meeting on Association for Computational Linguistics
A rule-based approach to prepositional phrase attachment disambiguation
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 2
An unsupervised model for statistically determining coordinate phrase attachment
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on 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
Identifying Nocuous Ambiguities in Natural Language Requirements
RE '06 Proceedings of the 14th IEEE International Requirements Engineering Conference
Using the web as an implicit training set: application to structural ambiguity resolution
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
A methodology for automatic identification of nocuous ambiguity
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Extending Nocuous Ambiguity Analysis for Anaphora in Natural Language Requirements
RE '10 Proceedings of the 2010 18th IEEE International Requirements Engineering Conference
A methodology for automatic identification of nocuous ambiguity
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
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Natural language is prevalent in requirements documents. However, ambiguity is an intrinsic phenomenon of natural language, and is therefore present in all such documents. Ambiguity occurs when a sentence can be interpreted differently by different readers. In this paper, we describe an automated approach for characterizing and detecting so-called nocuous ambiguities, which carry a high risk of misunderstanding among different readers. Given a natural language requirements document, sentences that contain specific types of ambiguity are first extracted automatically from the text. A machine learning algorithm is then used to determine whether an ambiguous sentence is nocuous or innocuous, based on a set of heuristics that draw on human judgments, which we collected as training data. We implemented a prototype tool for Nocuous Ambiguity Identification (NAI), in order to illustrate and evaluate our approach. The tool focuses on coordination ambiguity. We report on the results of a set of experiments to assess the performance and usefulness of the approach.