Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Khmer Text Classification Using Word Embedding and Neural Networks   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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The categories of this paper are: solv-int
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The categories of this paper are: patt-sol
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The categories of this paper are: supr-con
31
The categories of this paper are: ao-sci
4
The categories of this paper are: cond-mat
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The categories of this paper are: adap-org
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The categories of this paper are: hep-lat
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The categories of this paper are: nucl-ex
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The categories of this paper are: econ
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The categories of this paper are: astro-ph
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The categories of this paper are: hep-th
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The categories of this paper are: acc-phys
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The categories of this paper are: plasm-ph
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The categories of this paper are: physics
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The categories of this paper are: q-alg
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The categories of this paper are: dg-ga