• Title/Summary/Keyword: Char2Vec

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The Sentence Similarity Measure Using Deep-Learning and Char2Vec (딥러닝과 Char2Vec을 이용한 문장 유사도 판별)

  • Lim, Geun-Young;Cho, Young-Bok
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.22 no.10
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    • pp.1300-1306
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    • 2018
  • The purpose of this study is to see possibility of Char2Vec as alternative of Word2Vec that most famous word embedding model in Sentence Similarity Measure Problem by Deep-Learning. In experiment, we used the Siamese Ma-LSTM recurrent neural network architecture for measure similarity two random sentences. Siamese Ma-LSTM model was implemented with tensorflow. We train each model with 200 epoch on gpu environment and it took about 20 hours. Then we compared Word2Vec based model training result with Char2Vec based model training result. as a result, model of based with Char2Vec that initialized random weight record 75.1% validation dataset accuracy and model of based with Word2Vec that pretrained with 3 million words and phrase record 71.6% validation dataset accuracy. so Char2Vec is suitable alternate of Word2Vec to optimize high system memory requirements problem.