  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.
