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학술지 Domain-Adaptation Technique for Semantic Role Labeling with Structural Learning
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저자
임수종, 이창기, 류법모, 김현기, 박상규, 나동렬
발행일
201411
출처
ETRI Journal, v.36 no.3, pp.429-438
ISSN
1225-6463
출판사
한국전자통신연구원 (ETRI)
DOI
https://dx.doi.org/10.4218/etrij.14.0113.0645
협약과제
14MS4400, 휴먼 지식증강 서비스를 위한 지능진화형 Wise QA 플랫폼 기술 개발, 박상규
초록
Semantic role labeling (SRL) is a task in natural-language processing with the aim of detecting predicates in the text, choosing their correct senses, identifying their associated arguments, and predicting the semantic roles of the arguments. Developing a high-performance SRL system for a domain requires manually annotated training data of large size in the same domain. However, such SRL training data of sufficient size is available only for a few domains. Constructing SRL training data for a new domain is very expensive. Therefore, domain adaptation in SRL can be regarded as an important problem. In this paper, we show that domain adaptation for SRL systems can achieve state-of-The-Art performance when based on structural learning and exploiting a prior model approach. We provide experimental results with three different target domains showing that our method is effective even if training data of small size is available for the target domains. According to experimentations, our proposed method outperforms those of other research works by about 2% to 5% in F-score.
키워드
Domain adaptation, Natural language, Prior model, Semantic analysis, Semantic role labeling, Structured learning
KSP 제안 키워드
Art performance, F-score, High performance, Natural Language Processing, Structured learning, domain adaptation, large size, prior model, semantic analysis, semantic role labeling, small size