Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/76618
Title: Pattern-based rule disambiguation
Authors: Zheng, J
Cheng, G
Li, S
Kong, F
Huang, CR 
Zhou, G
Keywords: Natural Language Processing
Pattern-based approach
Rules-based approaches
Issue Date: 2016
Publisher: Institute of Electrical and Electronics Engineers
Source: 12th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2015, Zhangjiajie, China, 15 - 17 August 2015, 7382156, p. 1444-1449 How to cite?
Abstract: The biggest challenges to rules-based approaches to Natural Language Processing (NLP) are the resources required to do an exhaustive search for rule-matching, and the decision to select the optimal rule when there are multiple possible matches. In this paper, we propose a novel approach named pattern-based rule disambiguation (PRD) to face these challenges. PRD helps to determine which rule is activated by a pattern when the pattern activates more than one rule. To tackle this task, we first collect and annotate the samples following the same pattern, but activating different rulesMergeCell Then, we leverage the corpus to train a statistic classifier to disambiguate the pattern. This new approach is applied to the task of emotion cause detection, adopting a linguistic rule-drive paradigm which was the only one available for this task. The experimental results demonstrated the effectiveness of our PRD approach and offered a promising solution of the resolution of multiple-matched rules challenge for future NLP tasks.
URI: http://hdl.handle.net/10397/76618
ISBN: 9781467376822
DOI: 10.1109/FSKD.2015.7382156
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