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RatVec: A General Approach for Low-dimensional Distributed Vector Representations via Rational Kernels

 
: Brito, Eduardo; Georgiev, Bogdan; Domingo-Fernández, Daniel; Hoyt, Charles Tapley; Bauckhage, Christian

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Volltext ()

Jäschke, Robert:
Conference on "Lernen, Wissen, Daten, Analysen", LWDA 2019. Proceedings. Online resource : Berlin, Germany, September 30 - October 2, 2019
Berlin, 2019 (CEUR Workshop Proceedings 2454)
http://ceur-ws.org/Vol-2454/
S.74-78
Conference "Lernen, Wissen, Daten, Analysen" (LWDA) <2019, Berlin>
Englisch
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IAIS ()
Fraunhofer SCAI ()
representation learning; Kernel Principal Component Analysis; bioinformatic; natural language processing

Abstract
We present a general framework, RatVec, for learning vector representations of non-numeric entities based on domain-specific similarity functions interpreted as rational kernels. We show competitive performance using k-nearest neighbors in the protein family classification task and in Dutch spelling correction. To promote re-usability and extensibility, we have made our code and pre-trained models available athttps://github.com/ratvec.

: http://publica.fraunhofer.de/dokumente/N-562212.html