NDLI: Automatic detection of nocuous coordination ambiguities in natural language requirements

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Automatic detection of nocuous coordination ambiguities in natural language requirements

Content Provider	ACM Digital Library
Author	Nuseibeh, Bashar Willis, Alistair Yang, Hui De Roeck, Anne
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.
Starting Page	53
Ending Page	62
Page Count	10
File Format	PDF
ISBN	9781450301169
DOI	10.1145/1858996.1859007
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2010-09-20
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Human judgments Coordination ambiguity Nocuous ambiguity Machine learning Natural language requirements
Content Type	Text
Resource Type	Article

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