Active, Semi-Supervised Learning for Textual Information Access

Abstract : MACHINE learning techniques have been used for various tasks of document management and textual information access, such as categorisation, information extraction, or automatic organization of large document collections. Acquiring the annotated data necessary to apply supervised learning techniques is a major challenge for text applications, especially in very large collections. Annotating textual data usually requires humans who can read and understand the texts, and is therefore very costly, especially in technical domains. In this contribution, we address the problem or reducing this annotation burden.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-01352577
Contributor : Lip6 Publications <>
Submitted on : Monday, August 8, 2016 - 3:25:49 PM
Last modification on : Thursday, March 21, 2019 - 1:10:04 PM

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  • HAL Id : hal-01352577, version 1

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Anastasia Krithara, Cyril Goutte, Massih-Reza Amini, Jean-Michel Renders. Active, Semi-Supervised Learning for Textual Information Access. International Workshop on Intelligent Information Access (IIIA 2006), Jul 2006, Helsinki, Finland. pp.24-25. ⟨hal-01352577⟩

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