LUKe and MIKe: learning from user knowledge and managing interactive knowledge extraction
Pages 2671 - 2673
Abstract
Semantic recognition and annotation of unqiue enities and their relations is a key in understanding the essence contained in large text corpora. It typically requires a combination of efficient automatic methods and manual verification. Usually, both parts are seen as consecutive steps. In this demo we present MIKE, a user interface enabling the integration of user feedback into an iterative extraction process. We show how an extraction system can directly learn from such integrated user supervision. In general, this setup allows for stepwise training of the extraction system to a particular domain, while using user feedback early in the iterative extraction process improves extraction quality and reduces the overall human effort needed.
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Index Terms
- LUKe and MIKe: learning from user knowledge and managing interactive knowledge extraction
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Information
Published In
October 2012
2840 pages
ISBN:9781450311564
DOI:10.1145/2396761
- General Chair:
- Xuewen Chen,
- Program Chairs:
- Guy Lebanon,
- Haixun Wang,
- Mohammed J. Zaki
Copyright © 2012 Authors.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 29 October 2012
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CIKM'12: 21st ACM International Conference on Information and Knowledge Management
October 29 - November 2, 2012
Hawaii, Maui, USA
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