Usable Amharic text corpus for natural language processing applications

In this paper, we describe the preparation of a usable Amharic text corpus for different Natural Language Processing (NLP) applications. Natural language applications, such as document classification, topic modeling, machine translation, speech recognition, and others, suffer greatly from a lack of digital resources. This is especially true for Amharic, a resource-constrained, morphologically rich, and complex language. In response to this, a total of 67,739 Amharic news documents consisting of 8 different categories from online sources are collected. The collected corpus passes through a number of pre-processing steps including; data cleaning, text normalization and punctuation correction. To validate the usability of the collected corpora from different domains, a baseline document classification experiment was conducted. Experimental results show that, 84.53% accuracy is registered using deep learning in the absence of linguistic information. Finding indicated that it is possible to use the prepared corpora for different natural language applications in the absence of linguistic resources such as stemmer and dictionary despite the complexity of Amharic language. We are further working towards Amharic news document classification by incorporating a linguistic independent stop-word detection, stemming and unsupervised morphological segmentation of Amharic documents.