Improving part-of-speech tagging in Amharic language using deep neural network
Sintayehu Hirpassa, Gurpreet Singh Lehal
Abstract
To date, several POS taggers have been introduced to facilitate the success of semantic analysis for different languages. However, the task of POS tagging becomes a bit intricate in morphologically complex languages, like Amharic. In this paper, we evaluated different models such as bidirectional long short term memory, convolutional neural network in combination with bidirectional long short term memory, and conditional random field for Amharic POS tagging. Various features, both language-dependent and -independent, have been explored in a conditional random field model. Besides, word-level and character-level features are analyzed in deep neural network models. A convolutional neural network is utilized for encoding features at the word and character level. Each model's performance has evaluated on the dataset that contained 321 K tokens and manually tagged with 31 POS tags. Lastly, the best performance obtained by an end-to-end deep neural network model, convolutional neural network in combination with bidirectional long term short memory and conditional random field, is 97.23% accuracy. This is the highest accuracy for Amharic POS tagging task and is competent with contemporary taggers currently existing in different languages.