Abstract
In order to improve the performance of a deep-learning neural network, the paper outlines a stack-based approach incorporating various information sources. A named entity recognition system for Amharic was implemented using a recurrent neural network, a bi-directional long short term memory model. Word vectors based on semantic information were built using an unsupervised learning algorithm, word2vec, while a Conditional Random Fields (CRF) classifier was trained on language independent features to predict each token’s named entity class. The predictions, features and word vectors were fed to the deep neural network to assign labels to the words. This stack-based approach reached an 74.26% F-score, outperforming various other deep-learning set-ups, as well as a baseline CRF classifier, and an ensemble method incorporating the same information sources.
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Notes
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The CIA World Factbook estimates Ethiopia’s population to currently be 102.4 million, with 27% having Amharic as first language (https://www.cia.gov/library/publications/the-world-factbook/geos/et.html), while Hudson [12] claimed Amharic to be understood by about 40% of the Ethiopians—at least at that time.
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Acknowledgements
Thanks to Lars Bungum, Biswanath Barik, the anonymous reviewers, and our project partners at Masaryk University (Brno, Czech Republic), Addis Ababa University (Ethiopia), and University of Oslo (Norway). This work was carried out within the HaBiT project (“Harvesting big text data for under-resourced languages”: http://www.habit-project.eu) funded by the Research Council of Norway (NFR) and the Czech Ministry of Education, Youth and Sports (MŠMT) through the CZ09 Czech-Norwegian Research Programme and the EEA/Norway Financial Mechanism under Project Contract 7F14047.
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Sikdar, U.K., Gambäck, B. (2018). Named Entity Recognition for Amharic Using Stack-Based Deep Learning. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2017. Lecture Notes in Computer Science(), vol 10761. Springer, Cham. https://doi.org/10.1007/978-3-319-77113-7_22
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