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Published in:
2015 | OriginalPaper | Chapter
On Designing an Effective Training Set for Information Extraction
Authors : Young-Min Kim, Sa-kwang Song, Sungho Shin, Choong-Nyoung Seon, Seunggyun Hong, Hanmin Jung
Published in: Computer Science and its Applications
Publisher: Springer Berlin Heidelberg
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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.