We propose a syllable-based learning model for Korean using a convolutional neural network, in which word representation is composed of trained syllable vectors. Our model successfully produces morphologi- cally meaningful representation of Korean words compared to the original Skip-gram embeddings.
Aug 5, 2017 · We propose a syllable-based learning model for Korean using a convolutional neural network, in which word representation is composed of trained ...
This work proposes a syllable-based learning model for Korean using a convolutional neural network, in which word representation is composed of trained ...
A Syllable-based Technique for Word Embeddings of Korean Words
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We propose a syllable-based learning model for Korean using a convolutional neural network, in which word representation is composed of trained syllable vectors ...
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Aug 5, 2017 · We propose a syllable-based learning model for Korean using a convolutional neural network, in which word representation is composed of trained ...
This repository contains the re-implmentation of the paper 'A Syllable-based Technique for Word Embeddings of Korean Words' on AllenNLP and PyTorch. We ...
A Syllable-based Technique for Word Embeddings of Korean Words no code implementations • WS 2017 • Sanghyuk Choi, Taeuk Kim, Jinseok Seol, Sang-goo Lee
[W1] A Syllable-based Technique for Word Embeddings of Korean Words. Sanghyuk Choi, Taeuk Kim, Jinseok Seol, Sang-goo Lee. The 1st Workshop on Subword and ...
In this method, every input syllable is mapped to a single tag, called the POSMORPH tag, which is created with the corresponding POS tags and base morph forms ...
This work proposes an enhancement method that fully exploits the hierarchical Korean word embedding structure from source representation and achieves BLEU ...