2006 OnDemandInformationExtraction
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- (Sekine, 2006) ⇒ Satoshi Sekine. (2006). “On-Demand Information Extraction.” In: Proceedings of the 44th Annual Meeting of the Association for Computational Linguistics (ACL 2006).
Subject Headings: Open Information Extraction Task, TF-IDF, On-demand Information Extraction Task.
Notes
Cited By
- https://dl.acm.org/citation.cfm?id=1273167
- https://scholar.google.com/scholar?cluster=14781593366312179922&as_sdt=0,5
- ~53 http://scholar.google.com/scholar?cites=1650585050514518408
2007
- (Banko et al., 2007) ⇒ Michele Banko, Michael J. Cafarella, Stephen Soderland, Matt Broadhead, and Oren Etzioni. (2007). “Open Information Extraction from the Web.” In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI 2007).
- QUOTE: This year, (Sekine, 2006) proposed a paradigm for “on-demand information extraction,” which aims to eliminate customization involved with adapting IE systems to new topics. Using unsupervised learning methods, the system automatically creates patterns and performs extraction based on a topic that has been specified by a user.
Quotes
Abstract
At present, adapting an Information Extraction system to new topics is an expensive and slow process, requiring some knowledge engineering for each new topic. We propose a new paradigm of Information Extraction which operates 'on demand' in response to a user's query. On-demand Information Extraction (ODIE) aims to completely eliminate the customization effort. Given a user’s query, the system will automatically create patterns to extract salient relations in the text of the topic, and build tables from the extracted information using paraphrase discovery technology. It relies on recent advances in pattern discovery, paraphrase discovery, and extended named entity tagging. We report on experimental results in which the system created useful tables for many topics, demonstrating the feasibility of this approach.
References
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- DUC Home Page: http://duc.nist.gov
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