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.

[1]  Jiawei Han,et al.  BIDE: efficient mining of frequent closed sequences , 2004, Proceedings. 20th International Conference on Data Engineering.

[2]  Chin-Yew Lin,et al.  Better Binarization for the CKY Parsing , 2008, EMNLP.

[3]  Wu Hua,et al.  Improving statistical word alignment with a rule-based machine translation system , 2004, COLING 2004.

[4]  Dan I. Moldovan,et al.  Acquisition of Linguistic Patterns for Knowledge-Based Information Extraction , 1995, IEEE Trans. Knowl. Data Eng..

[5]  Raymond J. Mooney,et al.  Relational Learning of Pattern-Match Rules for Information Extraction , 1999, CoNLL.

[6]  Katja Hofmann,et al.  Lexical Patterns or Dependency Patterns: Which Is Better for Hypernym Extraction? , 2009, CoNLL.

[7]  Chu-Ren Huang,et al.  Emotion Cause Detection with Linguistic Constructions , 2010, COLING.

[8]  Sebastian Blohm,et al.  Large-scale pattern-based information extraction from the world wide web , 2011 .

[9]  Charu C. Aggarwal,et al.  XRules: an effective structural classifier for XML data , 2003, KDD '03.

[10]  Chu-Ren Huang,et al.  DETECTING EMOTION CAUSES WITH A LINGUISTIC RULE‐BASED APPROACH 1 , 2013, Comput. Intell..

[11]  Kai Wang,et al.  Exploiting Salient Patterns for Question Detection and Question Retrieval in Community-based Question Answering , 2010, COLING.

[12]  Bing Liu,et al.  Identifying comparative sentences in text documents , 2006, SIGIR.

[13]  Kai Wang,et al.  A syntactic tree matching approach to finding similar questions in community-based qa services , 2009, SIGIR.

[14]  Chu-Ren Huang,et al.  A Text-driven Rule-based System for Emotion Cause Detection , 2010, HLT-NAACL 2010.

[15]  Young-In Song,et al.  Finding question-answer pairs from online forums , 2008, SIGIR '08.

[16]  Eric Brill,et al.  A Simple Rule-Based Part of Speech Tagger , 1992, HLT.

[17]  Ming Zhou,et al.  Mining Sequential Patterns and Tree Patterns to Detect Erroneous Sentences , 2007, AAAI.

[18]  Tat-Seng Chua,et al.  Learning pattern rules for Chinese named entity extraction , 2002, AAAI/IAAI.

[19]  Fabio Ciravegna,et al.  Adaptive Information Extraction from Text by Rule Induction and Generalisation , 2001, IJCAI.

[20]  Hua Wu,et al.  Improving Statistical Word Alignment with a Rule-Based Machine Translation System , 2004, COLING.