Yang, Hui; Willis, Alistair; De Roeck, Anne and Nuseibeh, Bashar
(2010).
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| DOI (Digital Object Identifier) Link: | http://dx.doi.org/doi:10.1145/1858996.1859007 |
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| Google Scholar: | Look up in Google Scholar |
Abstract
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.
| Item Type: | Conference Item |
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| Copyright Holders: | 2010 ACM |
| ISBN: | 1-4503-0116-9, 978-1-4503-0116-9 |
| Keywords: | natural language requirements; nocuous ambiguity; coordination ambiguity; machine learning; human judgments |
| Academic Unit/Department: | Mathematics, Computing and Technology > Computing Mathematics, Computing and Technology |
| Interdisciplinary Research Centre: | Centre for Research in Computing (CRC) |
| Item ID: | 23771 |
| Depositing User: | Hui Yang |
| Date Deposited: | 27 Oct 2010 11:24 |
| Last Modified: | 12 Dec 2012 20:04 |
| URI: | http://oro.open.ac.uk/id/eprint/23771 |
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