상세정보
학술지
Domain-Adaptation Technique for Semantic Role Labeling with Structural Learning
- 발행일
- 201411
- 출처
- ETRI Journal, v.36 no.3, pp.429-438
- ISSN
- 1225-6463
- 출판사
- 한국전자통신연구원 (ETRI)
- 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.
- KSP 제안 키워드
- Art performance, F-score, High performance, Natural Language Processing, domain adaptation, large size, prior model, semantic role labeling, small size, state-of-The-Art, structural learning