Abstract
Relation extraction is a key step to address the problem of structuring natural language text. This paper proposes a new ontology class hierarchy feature to improve relation extraction when applying a method based on the distant supervision approach. It argues in favour of the expressiveness of the feature, in multi-class perceptrons, by experimentally showing its effectiveness when compared with combinations of (regular) lexical features.
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Notes
- 1.
Perceptron is a linear classifier for supervised machine learning. It is an assembly of linear-discriminant representations in which learning is based on error-correction.
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Acknowledgments
This work was partly funded by CNPq, under grants 312138/ 2013-0 and 303332/2013-1, and by FAPERJ, under grant E-26/201.337 /2014.
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Assis, P.H.R., Casanova, M.A., Laender, A.H.F., Milidiu, R. (2015). Improving Relation Extraction by Using an Ontology Class Hierarchy Feature. In: Wang, J., et al. Web Information Systems Engineering – WISE 2015. WISE 2015. Lecture Notes in Computer Science(), vol 9419. Springer, Cham. https://doi.org/10.1007/978-3-319-26187-4_20
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DOI: https://doi.org/10.1007/978-3-319-26187-4_20
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