A High Accuracy Method for Semi-supervised Information Extraction

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Customization to specific domains of dis-course and/or user requirements is one of the greatest challenges for today’s Information Extraction (IE) systems. While demonstrably effective, both rule-based and supervised machine learning approaches to IE customization pose too high a burden on the user. Semi-supervised learning approaches may in principle offer a more resource effective solution but are still insufficiently accurate to grant realistic application. We demonstrate that this limitation can be overcome by integrating fully-supervised learning techniques within a semi-supervised IE approach, without increasing resource requirements.

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Tratz, Stephen C. & Sanfilippo, Antonio P. April 22, 2007.

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Customization to specific domains of dis-course and/or user requirements is one of the greatest challenges for today’s Information Extraction (IE) systems. While demonstrably effective, both rule-based and supervised machine learning approaches to IE customization pose too high a burden on the user. Semi-supervised learning approaches may in principle offer a more resource effective solution but are still insufficiently accurate to grant realistic application. We demonstrate that this limitation can be overcome by integrating fully-supervised learning techniques within a semi-supervised IE approach, without increasing resource requirements.

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  • Proceedings of Human Language Technologies: The Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT 2007), 169-172

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  • Report No.: PNNL-SA-53858
  • Grant Number: AC05-76RL01830
  • Office of Scientific & Technical Information Report Number: 908955
  • Archival Resource Key: ark:/67531/metadc879133

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Office of Scientific & Technical Information Technical Reports

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  • April 22, 2007

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  • Sept. 22, 2016, 2:13 a.m.

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  • Oct. 28, 2016, 1:14 p.m.

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Tratz, Stephen C. & Sanfilippo, Antonio P. A High Accuracy Method for Semi-supervised Information Extraction, article, April 22, 2007; (digital.library.unt.edu/ark:/67531/metadc879133/: accessed November 18, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.