On Designing an Effective Training Set for Information Extraction

Title
On Designing an Effective Training Set for Information Extraction
Authors
김영민
Keywords
Training set; Information Extraction; Relation & Event Extraction
Issue Date
2015-01
Publisher
Springer
Citation
Lecture Notes in Electrical Engineering, v. 330, page. 1101-1107
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.
URI
http://hdl.handle.net/20.500.11754/23277https://link.springer.com/chapter/10.1007/978-3-662-45402-2_156
ISSN
1876-1100
DOI
http://dx.doi.org/10.1007/978-3-662-45402-2_156
Appears in Collections:
GRADUATE SCHOOL OF TECHNOLOGY & INNOVATION MANAGEMENT[S](기술경영전문대학원) > ETC
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