Khmer Text Classification Using Word Embedding and Neural Networks

• URL: http://arxiv.org/abs/2112.06748v1
• Date: Mon, 13 Dec 2021 15:57:32 GMT
• Title: Khmer Text Classification Using Word Embedding and Neural Networks
• Authors: Rina Buoy and Nguonly Taing and Sovisal Chenda
• Abstract summary: We discuss various classification approaches for Khmer text. A Khmer word embedding model is trained on a 30-million-Khmer-word corpus to construct word vector representations. We evaluate the performance of different approaches on a news article dataset for both multi-class and multi-label text classification tasks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Text classification is one of the fundamental tasks in natural language processing to label an open-ended text and is useful for various applications such as sentiment analysis. In this paper, we discuss various classification approaches for Khmer text, ranging from a classical TF-IDF algorithm with support vector machine classifier to modern word embedding-based neural network classifiers including linear layer model, recurrent neural network and convolutional neural network. A Khmer word embedding model is trained on a 30-million-Khmer-word corpus to construct word vector representations that are used to train three different neural network classifiers. We evaluate the performance of different approaches on a news article dataset for both multi-class and multi-label text classification tasks. The result suggests that neural network classifiers using a word embedding model consistently outperform the traditional classifier using TF-IDF. The recurrent neural network classifier provides a slightly better result compared to the convolutional network and the linear layer network.

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