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2019
Conference Paper
Titel
RatVec: A General Approach for Low-dimensional Distributed Vector Representations via Rational Kernels
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.
Author(s)