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 rules; 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.
| Original language | English |
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| Title of host publication | 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2015 |
| Publisher | IEEE |
| Pages | 1444-1449 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781467376822 |
| DOIs | |
| Publication status | Published - 13 Jan 2016 |
| Event | 12th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2015 - Zhangjiajie, China Duration: 15 Aug 2015 → 17 Aug 2015 |
Conference
| Conference | 12th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2015 |
|---|---|
| Country/Territory | China |
| City | Zhangjiajie |
| Period | 15/08/15 → 17/08/15 |
Keywords
- Natural Language Processing
- Pattern-based approach
- Rules-based approaches
ASJC Scopus subject areas
- Discrete Mathematics and Combinatorics
- Modelling and Simulation
- Computer Networks and Communications
- Control and Systems Engineering