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Text Classification Based on Convolutional Neural Networks and Word Embedding for Low-Resource Languages: Tigrinya by Fesseha, Awet (author);Xiong, Shengwu (author);Emiru, Eshete Derb (author);Diallo, Moussa (author);Dahou, Abdelghani (author) is a Computer Science article available to read on EtoBox.
What is Text Classification Based on Convolutional Neural Networks and Word Embedding for Low-Resource Languages: Tigrinya about?
This article studies convolutional neural networks for Tigrinya (also referred to as Tigrigna), which is a family of Semitic languages spoken in Eritrea and northern Ethiopia. Tigrinya is a “low-resource” language and is notable in terms of the absence of comprehensive and free data. Furthermore, it is characterized as one of the most semantically and syntactically complex languages in the world, similar to other Semitic languages. To the best of our knowledge, no previous research has been conducted on the state-of-the-art embedding technique that is shown here. We investigate which word representation methods perform better in terms of learning for single-label text classification problems, which are common when dealing with morphologically rich and complex languages. Manually annotated datasets are used here, where one contains 30,000 Tigrinya news texts from various sources with six categories of “sport”, “agriculture”, “politics”, “religion”, “education”, and “health” and one unannotated corpus that contains more than six million words. In this paper, we explore pretrained word embedding architectures using various convolutional neural networks (CNNs) to predict class labels.
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- Author
- Fesseha, Awet (author);Xiong, Shengwu (author);Emiru, Eshete Derb (author);Diallo, Moussa (author);Dahou, Abdelghani (author)
- Publisher
- MDPI AG
- Published
- 2021
- Language
- EN
- Field
- Computer Science (Physical Sciences)