Willis, Alistair; Yang, Hui and De Roeck, Anne
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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),
|Academic Unit/Department:||Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Faculty of Science, Technology, Engineering and Mathematics (STEM)
|Interdisciplinary Research Centre:||Centre for Research in Computing (CRC)|
|Depositing User:||Alistair Willis|
|Date Deposited:||03 May 2012 08:29|
|Last Modified:||04 Oct 2016 11:37|
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