Pattern-based rule disambiguation

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.