The Open UniversitySkip to content

A methodology for automatic identification of nocuous ambiguity

Yang, Hui; De Roeck, Anne; Willis, Alistair and Nuseibeh, Bashar (2010). A methodology for automatic identification of nocuous ambiguity. In: The 23rd International Conference on Computational Linguistics (Coling 2010), 23-27 Aug 2010, Beijing, China, pp. 1218–1226.

Full text available as:
PDF (Version of Record) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Download (171kB)
Google Scholar: Look up in Google Scholar


Nocuous ambiguity occurs when a linguistic expression is interpreted differently by different readers in a given context. We present an approach to automatically identify nocuous ambiguity that is likely to lead to misunderstandings among readers. Our model is built on a machine learning architecture. It learns from a set of heuristics each of which predicts a factor that may lead a reader to favor a particular interpretation. An ambiguity threshold indicates the extent to which ambiguity can be tolerated in the application domain. Collections of human judgments are used to train heuristics and set ambiguity thresholds, and for evaluation. We report results from applying the methodology to coordination and anaphora ambiguity. Results show that the method can identify nocuous ambiguity in text, and may be widened to cover further types of ambiguity. We discuss approaches to evaluation.

Item Type: Conference or Workshop Item
Copyright Holders: 2010 The Authors
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Research Group: Centre for Research in Computing (CRC)
Item ID: 23770
Depositing User: Hui Yang
Date Deposited: 27 Oct 2010 11:09
Last Modified: 07 Dec 2018 10:16
Share this page:

Download history for this item

These details should be considered as only a guide to the number of downloads performed manually. Algorithmic methods have been applied in an attempt to remove automated downloads from the displayed statistics but no guarantee can be made as to the accuracy of the figures.

Actions (login may be required)

Policies | Disclaimer

© The Open University   contact the OU