Machine Learning Approaches for Amharic Parts-of-speech Tagging

Autor:	Gashaw, Ibrahim, Shashirekha, H L.
Rok vydání:	2020
Předmět:	Computer Science - Computation and Language Computer Science - Information Retrieval Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Part-of-speech (POS) tagging is considered as one of the basic but necessary tools which are required for many Natural Language Processing (NLP) applications such as word sense disambiguation, information retrieval, information processing, parsing, question answering, and machine translation. Performance of the current POS taggers in Amharic is not as good as that of the contemporary POS taggers available for English and other European languages. The aim of this work is to improve POS tagging performance for the Amharic language, which was never above 91%. Usage of morphological knowledge, an extension of the existing annotated data, feature extraction, parameter tuning by applying grid search and the tagging algorithms have been examined and obtained significant performance difference from the previous works. We have used three different datasets for POS experiments. Comment: 15th International Conference on Natural Language Processing (ICON-2018)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2001.03324 Zobrazit plný text záznamu View this record from Arxiv