Sentence Level Amharic Word Sense Disambiguation
DOI:
https://doi.org/10.54536/ajet.v1i2.531Keywords:
Deep Learning, Natural Language Preprocessing, WordNet, Word Sense DisambiguationAbstract
Lexical ambiguity, phonological ambiguity, structural ambiguity, referential ambiguity, semantic ambiguity, and orthographic ambiguity were all types of Amharic ambiguity. The other ambiguities were out of this research because the study focuses on lexical-semantic, orthographic, and semantic ambiguities. Until now, some experts have been researching the Amharic word sense disambiguation system. Recent research, on the other hand, did not take into account antonym, troponymy, holonomy, and homonym relationships in the WordNet; this problem was overcome by this study. Using a Deep Learning method, we are developing an Amharic word sense disambiguation model. We use a design science research strategy to close the gap, starting with problem identification and concluding with final communication. 159 ambiguous words, 1214 synsets, and 2164 sentence datasets were used to create three distinct Deep Learning algorithms in three separate experiments. Using the given dataset, the overall performance of the model is measured using performance metrics in precision, F-measure, and confusion matrix. In this study, LSTM, CNN, and Bi-LSTM obtained 94 percent, 95 percent, and 96 percent accuracy respectively in the third experiment, based on performance measurement.
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Abid, M., Habib, A., Ashraf, J., & Shahid, A. (2018). Urdu word sense disambiguation using machine learning approach. Cluster Computing, 21.
Assabie, M. A. and Y. (2014). Development of Amharic Morphological Analyzer Using Memory-Based Learning. 9th International Conference on NLP, PolTAL 2014, 32, 1–13.
Banerjee, S., & Pedersen, T. (2002). An adapted Lesk algorithm for word sense disambiguation using WordNet. International Conference on Intelligent Text Processing and Computational Linguistics, 136–145.
Dureti, S. B. (2017). A Generic Approach towards all Words Amharic Word Sense Disambiguation. Addis Ababa University.
Heo, Y., Kang, S., & Seo, J. (2020). Hybrid sense classification method for large-scale word sense disambiguation. IEEE Access, 8, 27247-27256.
Mieraf, M. (2019). Word Sense Disambiguation for Amharic Sentences using WordNet Hierarchy. Bahir Dar, Ethioipia: Unpublished Master thesis, Bahir Dar University.
Nguyen, A., Pham, K., Ngo, D., Ngo, T., & Pham, L. (2021, August). An analysis of state-of-the-art activation functions for supervised deep neural network. In 2021 International Conference on System Science and Engineering (ICSSE) (pp. 215-220). IEEE.
Pesaranghader, A., Pesaranghader, A., & Sokolova, M. (2018). One Single Deep Bidirectional LSTM Network for Word Sense Disambiguation of Text Data (pp. 96–107).
Wassie, G., Ramesh, B. P., Teferra, S., & Meshesha, M. (2014). A Word Sense Disambiguation Model for Amharic Words using Semi-Supervised Learning Paradigm. Science, Technology and Arts Research Journal, 3(3), 147-155.
Yang, B., & Mitchell, T. (2019). Leveraging knowledge bases in lstms for improving machine reading. ArXiv Preprint ArXiv:1902.09091.
Zobaed, S., Haque, M. E., Rabby, M. F., & Salehi, M. A. (2021). Senspick: sense picking for word sense disambiguation. 2021 IEEE 15th International Conference on Semantic Computing (ICSC), 318–324.
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Copyright (c) 2022 Dereje Senay Merawi , Tesfa Tegegne Yalewu, Yitbarek Worku Tamir
This work is licensed under a Creative Commons Attribution 4.0 International License.