Distant supervision from knowledge graphs
- Smirnova, Alisa Exascale Infolab, University of Fribourg, Switzerland
- Audiffren, Julien Exascale Infolab, University of Fribourg, Switzerland
- Cudré-Mauroux, Philippe Exascale Infolab, University of Fribourg, Switzerland
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2018
Published in:
- Encyclopedia of Big Data Technologies. - 2018, p. 1–7
English
In this chapter, we discuss approaches leveraging distant supervision for relation extraction. We start by introducing the key ideas behind distant supervision as well as their main shortcomings. We then discuss approaches that improve over the basic method, including approaches based on the at-least-one-principle along with their extensions for handling false negative labels, and approaches leveraging topic models. We also describe embeddings-based methods including methods leveraging convolutional neural networks. Finally, we discuss how to take advantage of auxiliary information to improve relation extraction.
- Faculty
- Faculté des sciences et de médecine
- Department
- Département d'Informatique
- Language
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- English
- Classification
- Computer science and technology
- License
-
License undefined
- Identifiers
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- RERO DOC 324400
- DOI 10.1007/978-3-319-63962-8_285-1
- Persistent URL
- https://folia.unifr.ch/unifr/documents/307723
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