Korean Anaphora Recognition System to Develop Healthcare Dialogue-Type Agent.
10.4258/hir.2014.20.4.272
- Author:
Junggi YANG
1
;
Youngho LEE
Author Information
1. Department of IT Convergence Engineering, Gachon University, Seongnam, Korea.
- Publication Type:Original Article
- Keywords:
Anaphora Resolution;
Anaphora Recognition;
Reference Resolution;
Dialogue Analysis;
Natural Language Processing
- MeSH:
Delivery of Health Care*;
Natural Language Processing;
Semantics
- From:Healthcare Informatics Research
2014;20(4):272-279
- CountryRepublic of Korea
- Language:English
-
Abstract:
OBJECTIVES: Anaphora recognition is a process to identify exactly which noun has been used previously and relates to a pronoun that is included in a specific sentence later. Therefore, anaphora recognition is an essential element of a dialogue agent system. In the current study, all the merits of rule-based, machine learning-based, semantic-based anaphora recognition systems were combined to design and realize a new hybrid-type anaphora recognition system with an optimum capacity. METHODS: Anaphora recognition rules were encoded on the basis of the internal traits of referred expressions and adjacent contexts to realize a rule-based system and to serve as a baseline. A semantic database, related to predicate instances of sentences including referred expressions, was constructed to identify semantic co-relationships between the referent candidates (to which semantic tags were attached) and the semantic information of predicates. This approach would upgrade the anaphora recognition system by reducing the number of referent candidates. Additionally, to realize a machine learning-based system, an anaphora recognition model was developed on the basis of training data, which indicated referred expressions and referents. The three methods were further combined to develop a new single hybrid-based anaphora recognition system. RESULTS: The precision rate of the rule-based systems was 54.9%. However, the precision rate of the hybrid-based system was 63.7%, proving it to be the most efficient method. CONCLUSIONS: The hybrid-based method, developed by the combination of rule-based and machine learning-based methods, represents a new system with enhanced functional capabilities as compared to other pre-existing individual methods.
