상세정보
학술지
Consolidation of Subtasks for Target Task in Pipelined NLP Model
- 발행일
- 201410
- 출처
- ETRI Journal, v.36 no.5, pp.704-713
- ISSN
- 1225-6463
- 출판사
- 한국전자통신연구원 (ETRI)
- 협약과제
- 13PR4300, 영상객체 인식기반 지식융합 서비스 플랫폼 개발, 조기성
- 초록
- Most natural language processing tasks depend on the outputs of some other tasks. Thus, they involve other tasks as subtasks. The main problem of this type of pipelined model is that the optimality of the subtasks that are trained with their own data is not guaranteed in the final target task, since the subtasks are not optimized with respect to the target task. As a solution to this problem, this paper proposes a consolidation of subtasks for a target task (CST2). In CST2, all parameters of a target task and its subtasks are optimized to fulfill the objective of the target task. CST2finds such optimized parameters through a backpropagation algorithm. In experiments in which text chunking is a target task and part-of-speech tagging is its subtask, CST2outperforms a traditional pipelined text chunker. The experimental results prove the effectiveness of optimizing subtasks with respect to the target task.
- KSP 제안 키워드
- All parameters, NLP model, Natural Language Processing, Optimized parameters, Part of Speech(POS), Part-Of-Speech Tagging, backpropagation algorithm, text chunking