The Open UniversitySkip to content

A generalised hybrid architecture for NLP

Willis, Alistair; Yang, Hui and De Roeck, Anne (2012). A generalised hybrid architecture for NLP. In: Workshop on Innovative Hybrid Approaches to the Processing of Textual Data, 23 April 2012, Avignon, France, Association for Computational Linguistics, pp. 97–105.

Full text available as:
Full text not publicly available
Due to copyright restrictions, this file is not available for public download
Click here to request a copy from the OU Author.
Google Scholar: Look up in Google Scholar


Many tasks in natural language processing require that sentences be classified from a set of discrete interpretations. In these cases, there appear to be great benefits in using hybrid systems which apply multiple analyses to the test cases. In this paper, we examine a general principle for building hybrid systems, based on combining the results of several, high precision heuristics. By generalising the results of systems for sentiment analysis and ambiguity recognition, we argue that if correctly combined, multiple techniques classify better than single techniques. More importantly, the combined techniques can be used in tasks where no single classification is appropriate.

Item Type: Conference Item
Copyright Holders: 2012 Association for Computational Linguistics
Extra Information: Proceedings of the Workshop on Innovative Hybrid Approaches to the Processing of Textual Data (Hybrid2012),
EACL 2012,
ISBN 978-1-937284-19-0
Academic Unit/Department: Mathematics, Computing and Technology > Computing & Communications
Mathematics, Computing and Technology
Other Departments > Vice-Chancellor's Office
Other Departments
Interdisciplinary Research Centre: Centre for Research in Computing (CRC)
Related URLs:
Item ID: 33610
Depositing User: Alistair Willis
Date Deposited: 03 May 2012 08:29
Last Modified: 26 Feb 2016 13:27
Share this page:

▼ Automated document suggestions from open access sources

Actions (login may be required)

Policies | Disclaimer

© The Open University   + 44 (0)870 333 4340