On Designing an Effective Training Set for Information Extraction
- Authors
- Kim, Young Min; Song, Sa-kwang; Shin, Sungho; Seon, CN; Hong, Seunggyun; Jung, Hanmin
- Issue Date
- Jan-2015
- Publisher
- Springer
- Keywords
- Information extraction; Relation & event extraction; Training set
- Citation
- Lecture Notes in Electrical Engineering, v.330, pp.1101 - 1107
- Indexed
- SCOPUS
- Journal Title
- Lecture Notes in Electrical Engineering
- Volume
- 330
- Start Page
- 1101
- End Page
- 1107
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/158030
- DOI
- 10.1007/978-3-662-45402-2_156
- ISSN
- 1876-1100
- Abstract
- While training set design has received less attention from academia compared to its significance, it becomes crucial in big data environments. We propose a novel way to construct a training set for information extraction. An effective data collection considering the trade-off between system quality and annotation difficulty is the core of the proposed approach. Instead of a random collection of data like usual systems, well-defined key expressions are used as sampling queries. This work is a part of an on-going R&D project and now in process of manual annotation that would be evaluated via final system quality.
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