--- title: "Introducing various Semantic Models for — Venkatesh; 2020" description: "Amharic is a morphologically rich Ethio-Semitic language." authors: ["Seid Muhie Yimam; Abinew Ali Ayele; Gopalakrishnan Venkatesh;", "Ibrahim Gashaw;", "Chris Biemann"] year: 2020 venue: "arXiv:2011.01154 · Future Internet 2021, 13, 275" source: "https://arxiv.org/abs/2011.01154" doi: "10.3390/fi13110275" arxiv: "2011.01154" licence: "CC BY 4.0 (arXiv)" licence_url: "http://creativecommons.org/licenses/by/4.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/en/papers/yimam-2020-introducing-various-semantic-models-amharic/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/yimam-2020-introducing-various-semantic-models-amharic/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2020](https://md.research.elamur.ai/free/en/papers/2020/page/3/) / Introducing various Semantic Models for Amharic: Experimentation and Evaluation with multiple Tasks and Datasets # Introducing various Semantic Models for Amharic: Experimentation and Evaluation with multiple Tasks and Datasets **Authors:** Seid Muhie Yimam; Abinew Ali Ayele; Gopalakrishnan Venkatesh;, [Ibrahim Gashaw;](https://md.research.elamur.ai/free/en/authors/ibrahim-gashaw/), [Chris Biemann](https://md.research.elamur.ai/free/en/authors/chris-biemann/) · **Year:** 2020 · **Venue:** arXiv:2011.01154 · Future Internet 2021, 13, 275 · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/) **Links:** [Publisher's page](https://doi.org/10.3390/fi13110275) · [PDF](https://arxiv.org/pdf/2011.01154) · [DOI 10.3390/fi13110275](https://doi.org/10.3390/fi13110275) · [arXiv 2011.01154](https://arxiv.org/abs/2011.01154) · [OpenAlex](https://openalex.org/W3096921982) **Citations:** 2 citations (openalex-snapshot · 2026-10-14) · [16](#cited-by) in our library, of the 140884 papers whose bibliographies we read ## Summary Amharic is a morphologically rich Ethio-Semitic language. Raw text in it is easy to find, but few semantic models exist, and those that do (fastText, XLMR, Multi-Flair) come from multilingual setups that suit it poorly. The authors assembled a general-purpose corpus from news sites, a focused web crawl, the Amharic Web Corpus, tweets and YouTube comments. They also built the first Amharic tokenizer/sentence segmenter and a script normalizer. On this corpus they fine-tuned two existing models and trained seven new ones: - word2Vec - a JoBimText distributional thesaurus (DT) - network embeddings of the DT graph - FLAIR string embeddings - a RoBERTa-style model All models were tested on POS tagging, NER, sentiment classification, and word similarity and relatedness. Models trained or adapted on their own corpus beat the off-the-shelf multilingual ones. Contextual models (FLAIR, AmRoBERTa) worked best for tagging, and DT-based network embeddings worked best for sentiment. No single representation won every task. Models, tools and benchmarks are released openly, which gives Amharic NLP a shared baseline. ## Key points - The corpus holds about 6.15 million sentences and over 335 million tokens. News was scraped daily from January to May 2020. - Nine semantic models were released: - AmWord2Vec (CBOW and Skip-gram, 300-dimensional) - AmDT, built with JoBimText - DT network embeddings, including Role2Vec - AmFlair and MultFlairFT - AmRoBERTa - POS tagging results: - Transformer embeddings roughly matched a CRF tagger. - On rare tags such as ADJPCS and VPSs, AmRoBERTa fell back to the nearest frequent tag, while the CRF recalled them correctly. - NER proved hard because Amharic has no capitalization cue and many names derive from ordinary nouns, verbs or adjectives. XLM embeddings labelled every token "Other". FLAIR-based models did best. - Amharic versions of WordSim353 and SimLex999 were produced: first machine-translated with the Google Translate API, then checked by native speakers. ## Interesting ideas - Spelling variants written with same-sounding Fidel characters are collapsed to the most frequent form before training, so one word does not split into several embeddings. — For Mova (tokens): A similar majority-variant normalization pass could unify spelling and character variants before lemma lookup, so lexicon entries are not fragmented. - A distributional thesaurus is treated as a graph, and node-embedding algorithms such as Role2Vec are run over it; on sentiment this beat contextual models. — For Mova (world): Mova could store lemma similarity as an explicit, inspectable graph and derive dense features from it, which keeps the provenance of every similarity link explainable. - Benchmarks are created cheaply by machine-translating established English similarity sets and then having native speakers validate them. — For Mova (learn): Silver data from an LLM teacher could follow the same pattern of automatic draft plus human audit, ideally with a recorded acceptance rate. - The error analysis contrasts a CRF that memorizes rare tags with a transformer that drifts toward frequent neighbours. This suggests the two model types have complementary failure modes. — For Mova (learn): When the perceptron and gparse parsers disagree, the vote could weight the memorizing model more for rare labels and include rare-label slices as dedicated evaluation checks. - The paper reports per task, not as one overall score, and concludes that no single representation fits every application. — For Mova (answer): Evaluation should report each reader and test type separately, so a gain on fables does not hide a loss on maths word problems. *Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.* Fields: [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/) Topics: [Part-of-speech tagging](https://md.research.elamur.ai/free/en/topics/part-of-speech-tagging/) ## Cited by Cited by 16 papers of our library cite it (of the 140884 whose bibliographies we read); 2 of them have a page here, 14 not yet. - [GeezSwitch: Language Identification in Typologically Related Low-resourced East African Languages](https://md.research.elamur.ai/free/en/papers/gaim-2022-geezswitch-language-identification-typologically-related/) - [Dense Retrieval for Low Resource Languages -- the Case of Amharic Language](https://md.research.elamur.ai/free/en/papers/yeshambel-2025-dense-retrieval-low-resource-languages/) ## Cite The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @misc{venkatesh2020introducing, title = {{Introducing various Semantic Models for Amharic: Experimentation and Evaluation with multiple Tasks and Datasets}}, author = {Venkatesh;, Seid Muhie Yimam; Abinew Ali Ayele; Gopalakrishnan and Gashaw;, Ibrahim and Biemann, Chris}, year = {2020}, doi = {10.3390/fi13110275}, eprint = {2011.01154}, archivePrefix = {arXiv}, url = {https://doi.org/10.3390/fi13110275} } ``` - [← previous Interpreting Verbal Irony: Linguistic Strategies and the Connection to the Type of Semantic Incongruity](https://md.research.elamur.ai/free/en/papers/ghosh-2020-interpreting-verbal-irony-linguistic-strategies/) - [next → Is MAP Decoding All You Need? The Inadequacy of the Mode in Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/eikema-2020-is-map-decoding-all-you/) - [↑ back to papers of 2020](https://md.research.elamur.ai/free/en/papers/2020/page/3/) ## References 10 references, 3 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.11.12.13.14.15.16. Camacho‑Collados, J.; Pilehvar, M.T. Embeddings in Natural Language Processing. In Proceedings of the 28th International Conference on Computational Linguistics: Tutorial Abstracts, Online, 12–13 December 2020; pp. 10–15; [CrossRef] Katharina Sienčnik, S. Adapting word2vec to Named Entity Recognition. In Proceedings of the 20th Nordic Conference of Computational Linguistics (NODALIDA 2015), Vilnius, Lithuania, 11–13 May 2015; pp. 239–243. Joshi, M.; Hart, E.; Vogel, M.; Ruvini, J.D. Distributed Word Representations Improve NER for e‑Commerce. In Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing, Denver, CO, USA, 31 May–5 June 2015; pp. 160– 167; [CrossRef] Hou, J.; Koppatz, M.; Quecedo, J.M.H.; Yangarber, R. Projecting named entity recognizers without annotated or parallel corpora. In Proceedings of the 22nd Nordic Conference on Computational Linguistics, Turku, Finland, 30 September–2 October 2019; pp. 232–241. Mbouopda, M.F.; Yonta, P.M. A Word Representation to Improve Named Entity Recognition in Low‑resource Languages. In Pro‑ ceedings of the 2019 Sixth International Conference on Social Networks Analysis, Management and Security (SNAMS), Granada, Spain, 22–25 October 2019; pp. 333–337; [CrossRef] Barhoumi, A.; Camelin, N.; Aloulou, C.; Estève, Y.; Hadrich Belguith, L. Toward Qualitative Evaluation of Embeddings for Arabic Sentiment Analysis. In Proceedings of the 12th Language Resources and Evaluation Conference, Marseille, France, 13– 15 May 2020; pp. 4955–4963. Al‑Saqqa, S.; Awajan, A. The Use of Word2vec Model in Sentiment Analysis: A Survey. In Proceedings of the 2019 International Conference on Artificial Intelligence, Robotics and Control, Cairo, Egypt, 14–16 December 2019; pp. 39–43; [CrossRef] Younes, A.; Weeds, J. Embed More Ignore Less (EMIL): Exploiting Enriched Representations for Arabic NLP. In Proceedings of the Fifth Arabic Natural Language Processing Workshop, Barcelona, Spain, 12 December 2020; pp. 139–154. Thavareesan, S.; Mahesan, S. Word embedding‑based Part of Speech tagging in Tamil texts. In Proceedings of the 2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS), Rupnagar, India, 26–28 November 2020; pp. 478–482; [CrossRef] Pickard, T. Comparing word2vec and GloVe for Automatic Measurement of MWE Compositionality. In Proceedings of the Joint Workshop on Multiword Expressions and Electronic Lexicons, Online, 13 December 2020; pp. 95–100. Jadi, G.; Claveau, V.; Daille, B.; Monceaux, L. Evaluating Lexical Similarity to build Sentiment Similarity. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16), Portorož, Slovenia, 23–28 May 2016; pp. 1196–1201. Devlin, J.; Chang, M.W.; Lee, K.; Toutanova, K. BERT: Pre‑training of Deep Bidirectional Transformers for Language Understand‑ ing. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Minneapolis, MN, USA, 2–7 June 2019; Volume 1, pp. 4171–4186. Tenney, I.; Das, D.; Pavlick, E. BERT Rediscovers the Classical NLP Pipeline. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy, 28 July–2 August 2019; pp. 4593–4601; [CrossRef] Agerri, R.; Vicente, I.S.; Campos, J.A.; Barrena, A.; Saralegi, X.; Soroa, A.; Agirre, E. Give your Text Representation Models some Love: The Case for Basque. arXiv 2020, arXiv:2004.00033. Ulčar, M.; Robnik‑Šikonja, M. High Quality ELMo Embeddings for Seven Less‑Resourced Languages. arXiv 2019, arXiv:1911.10049. Grave, E.; Bojanowski, P.; Gupta, P.; Joulin, A.; Mikolov, T. Learning Word Vectors for 157 Languages. In Proceedings of the International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan 7–12 May 2018; pp. 3483–3487. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40. 41. 42. 43. [↗](https://arxiv.org/abs/2004.00033) [our card](https://md.research.elamur.ai/free/en/papers/tenney-2019-bert-rediscovers-classical-nlp-pipeline/) 2. Conneau, A.; Khandelwal, K.; Goyal, N.; Chaudhary, V.; Wenzek, G.; Guzmán, F.; Grave, E.; Ott, M.; Zettlemoyer, L.; Stoyanov, V. Unsupervised Cross‑lingual Representation Learning at Scale. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Online, 5–10 July 2020; pp. 8440–8451. [find](https://scholar.google.com/scholar?q=Conneau%2C+A%2E%3B+Khandelwal%2C+K%2E%3B+Goyal%2C+N%2E%3B+Chaudhary%2C+V%2E%3B+Wenzek%2C+G%2E%3B+Guzm%C3%A1n%2C+F%2E%3B+Grave%2C+E%2E%3B+Ott%2C+M%2E%3B+Zettlemoyer%2C+L%2E%3B+Stoyanov%2C+V%2E+Unsupervised+Cross%E2%80%91lingual+Representation+Learning+at+Scale%2E+In+Proceed) 3. Schweter, S. Multilingual Flair Embeddings. 2020. Available online: https://github.com/flairNLP/flair‑lms (accessed on 24 October 2021). Gashaw, I.; Shashirekha, H.L. Machine Learning Approaches for Amharic Parts‑of‑speech Tagging. arXiv 2020, arXiv:2001.03324. Yimam, S.M.; Alemayehu, H.M.; Ayele, A.; Biemann, C. Exploring Amharic Sentiment Analysis from Social Media Texts: Build‑ ing Annotation Tools and Classification Models. In Proceedings of the 28th International Conference on Computational Lin‑ guistics, Barcelona, Spain, 8–13 December 2020; pp. 1048–1060; [CrossRef] Gezmu, A.M.; Seyoum, B.E.; Gasser, M.; Nürnberger, A. Contemporary Amharic Corpus: Automatically Morpho‑Syntactically Tagged Amharic Corpus. In Proceedings of the First Workshop on Linguistic Resources for Natural Language Processing, August 2018; pp. 65–70. Available online: https://aclanthology.org/W18‑3809.pdf (accessed on 24 October 2021). Salawu, A.; Aseres, A. Language policy, ideologies, power and the Ethiopian media. Communicatio 2015, 41, 71–89. [CrossRef] Gasser, M. HornMorpho: A system for morphological processing of Amharic, Oromo, and Tigrinya. In Proceedings of the Conference on Human Language Technology for Development, Alexandria, Egypt, 2–5 May 2011; pp. 94–99. Suchomel, V.; Rychlý, P. Amharic Web Corpus; LINDAT/CLARIN Digital Library at the Institute of Formal and Applied Linguis‑ tics (ÚFAL), Faculty of Mathematics and Physics, Charles University: Staré Město, Czechia, 2016. Harris, Z.S. Distributional Structure. Word 1954, 10, 146–162. [CrossRef] Ruppert, E.; Kaufmann, M.; Riedl, M.; Biemann, C. JoBimViz: A Web‑based Visualization for Graph‑based Distributional Se‑ mantic Models. In Proceedings of the ACL‑IJCNLP 2015 System Demonstrations, Beijing, China, 26–31 July 2015; pp. 103–108. Mikolov, T.; Sutskever, I.; Chen, K.; Corrado, G.; Dean, J. Distributed representations of words and phrases and their composi‑ tionality. arXiv 2013, arXiv:1310.4546. Řehúřek, R.; Sojka, P. Gensim—Statistical Semantics in Python. In Proceedings of the EuroScipy 2011. Available online: https://www.fi.muni.cz/usr/sojka/posters/rehurek‑sojka‑scipy2011.pdf (accessed on 24 October 2021). Hamilton, W.L.; Ying, R.; Leskovec, J. Representation Learning on Graphs: Methods and Applications. arXiv 2017, arXiv:1709.05584. Sevgili, Ö.; Panchenko, A.; Biemann, C. Improving Neural Entity Disambiguation with Graph Embeddings. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, Florence, Italy, 29 July–1 August 2019; pp. 315–322. Cai, H.; Zheng, V.W.; Chang, K.C.C. A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications. IEEE Trans. Knowl. [↗](https://arxiv.org/abs/2001.03324) 4. Data Eng. 2018, 30, 1616–1637. [CrossRef] Perozzi, B.; Al‑Rfou, R.; Skiena, S. DeepWalk: Online Learning of Social Representations. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA, 24–27 August 2014; pp. 701– 710. Ahmed, N.K.; Rossi, R.A.; Lee, J.B.; Willke, T.L.; Zhou, R.; Kong, X.; Eldardiry, H. role2vec: Role‑Based Network Embeddings. 2019; pp. 1–7. Available online: http://ryanrossi.com/pubs/role2vec‑DLG‑KDD.pdf (accessed on 24 October 2021). Rozemberczki, B.; Kiss, O.; Sarkar, R. An API Oriented Open‑source Python Framework for Unsupervised Learning on Graphs. arXiv 2020, arXiv:2003.04819. Akbik, A.; Blythe, D.; Vollgraf, R. Contextual String Embeddings for Sequence Labeling. In Proceedings of the 27th International Conference on Computational Linguistics, Santa Fe, NM, USA, 21–25 August 2018; pp. 1638–1649. Akbik, A.; Bergmann, T.; Blythe, D.; Rasul, K.; Schweter, S.; Vollgraf, R. FLAIR: An Easy‑to‑Use Framework for State‑of‑the‑Art NLP. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations), Minneapolis, MN, USA, 2–7 June 2019; pp. 54–59. Schweter, S.; Akbik, A. FLERT: Document‑Level Features for Named Entity Recognition. arXiv 2020, arXiv:2011.06993. Agić, Ž.; Vulić, I. JW300: A Wide‑Coverage Parallel Corpus for Low‑Resource Languages. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy, 28 July–2 August 2019; pp. 3204–3210. Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A.N.; Kaiser, L.U.; Polosukhin, I. Attention is All you Need. In Advances in Neural Information Processing Systems; Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R., Eds.; Curran Associates, Inc.: Red Hook, NY, USA, 2017; Volume 30. Liu, Y.; Ott, M.; Goyal, N.; Du, J.; Joshi, M.; Chen, D.; Levy, O.; Lewis, M.; Zettlemoyer, L.; Stoyanov, V. RoBERTa: A Robustly Optimized BERT Pretraining Approach. arXiv 2019, arXiv:1907.11692. Agirre, E.; Alfonseca, E.; Hall, K.; Kravalova, J.; Paşca, M.; Soroa, A. A Study on Similarity and Relatedness Using Distributional and WordNet‑based Approaches. In Proceedings of the Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Boulder, CO, USA, 31 May–5 June 2009; pp. 19–27. Netisopakul, P.; Wohlgenannt, G.; Pulich, A. Word similarity datasets for Thai: Construction and evaluation. [↗](https://arxiv.org/abs/2003.04819) [our card](https://md.research.elamur.ai/free/en/papers/akbik-2018-contextual-string-embeddings-sequence-labeling/) 5. IEEE Access 2019, 7, 142907–142915. [CrossRef] Asr, F.T.; Zinkov, R.; Jones, M. Querying word embeddings for similarity and relatedness. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, New Orleans, LA, USA, 1–6 June 2018; Volume 1, pp. 675–684. 44. 45. 46. 47. 48. 49. 50. 51. 52. 53. 54. 55. 56. 57. 58. 59. 60. 61. Hill, F.; Reichart, R.; Korhonen, A. Simlex‑999: Evaluating semantic models with (genuine) similarity estimation. Comput. [find](https://scholar.google.com/scholar?q=IEEE+Access+2019%2C+7%2C+142907%E2%80%93142915%2E+%5BCrossRef%5D+Asr%2C+F%2ET%2E%3B+Zinkov%2C+R%2E%3B+Jones%2C+M%2E+Querying+word+embeddings+for+similarity+and+relatedness%2E+In+Proceedings+of+the+2018+Conference+of+the+North+American+Cha) [our card](https://md.research.elamur.ai/free/en/papers/hill-2015-simlex-999-evaluating-semantic-models/) 6. Linguist. 2015, 41, 665–695. [CrossRef] Speer, R.; Chin, J.; Havasi, C. ConceptNet 5.5: An Open Multilingual Graph of General Knowledge. In Proceedings of the Thirty‑First AAAI Conference on Artificial Intelligence, San Francisco, CA, USA, 4–9 February 2017; pp. 4444–4451. Recski, G.; Iklódi, E.; Pajkossy, K.; Kornai, A. Measuring Semantic Similarity of Words Using Concept Networks. In Proceedings of the 1st Workshop on Representation Learning for NLP, Berlin, Germany, 11 August 2016; pp. 193–200; [CrossRef] Getachew, M. Automatic Part‑of‑Speech Tagging for Amharic Language an Experiment Using Stochastic Hidden Markov Ap‑ proach. Master’s Thesis, School of Graduate Studies, Addis Ababa University, Addis Ababa, Ethiopia, 2001. Gambäck, B.; Olsson, F.; Alemu Argaw, A.; Asker, L. Methods for Amharic Part‑of‑Speech Tagging. In Proceedings of the First Workshop on Language Technologies for African Languages, Athens, Greece, 31 March 2009; pp. 104–111. Tachbelie, M.Y.; Menzel, W. Amharic Part‑of‑Speech Tagger for Factored Language Modeling. In Proceedings of the Interna‑ tional Conference RANLP‑2009, Borovets, Bulgaria, 14–16 September 2009; pp. 428–433. Tachbelie, M.Y.; Abate, S.T.; Besacier, L. Part‑of‑speech tagging for underresourced and morphologically rich languages—The case of Amharic. HLTD 2011 , 50–55. Available online: [https://www.cle.org.pk/hltd/pdf/HLTD201109.pdf](https://www.cle.org.pk/hltd/pdf/HLTD201109.pdf) (accessed on 24 October 2021). Demeke, G.; Getachew, M. Manual Annotation of Amharic News Items with Part‑of‑Speech Tags and Its Challenges. [↗](https://www.cle.org.pk/hltd/pdf/HLTD201109.pdf) 7. ELRC Working Paper; 2006; Volume 2, pp. 1–16. Available online: [https://www.bibsonomy.org/bibtex/d2fa6b0ccf8737fb4046c3d13f274894#](https://www.bibsonomy.org/bibtex/d2fa6b0ccf8737fb4046c3d13f274894#) export%7D%7B (accessed on 24 October 2021). Zitouni, I. Natural Language Processing of Semitic Languages; Springer: Berlin/Heidelberg, Germany, 2014. Ahmed, M. Named Entity Recognition for Amharic Language. [↗](https://www.bibsonomy.org/bibtex/d2fa6b0ccf8737fb4046c3d13f274894#) 8. Master’s Thesis, Addis Ababa University, Addis Ababa, Ethiopia, 2010. Alemu, B. A Named Entity Recognition for Amharic. [find](https://scholar.google.com/scholar?q=Master%E2%80%99s+Thesis%2C+Addis+Ababa+University%2C+Addis+Ababa%2C+Ethiopia%2C+2010%2E+Alemu%2C+B%2E+A+Named+Entity+Recognition+for+Amharic%2E) 9. Master’s Thesis, Addis Ababa University, Addis Ababa, Ethiopia, 2013. Tadele, M. Amharic Named Entity Recognition Using a Hybrid Approach. [find](https://scholar.google.com/scholar?q=Master%E2%80%99s+Thesis%2C+Addis+Ababa+University%2C+Addis+Ababa%2C+Ethiopia%2C+2013%2E+Tadele%2C+M%2E+Amharic+Named+Entity+Recognition+Using+a+Hybrid+Approach%2E) 10. Master’s Thesis, Addis Ababa University, Addis Ababa, Ethiopia, 2014. Gambäck, B.; Sikdar, U.K. Named entity recognition for Amharic using deep learning. In Proceedings of the 2017 IST‑Africa Week Conference (IST‑Africa), Windhoek, Namibia, 31 May–2 June 2017; pp. 1–8. Sikdar, U.K.; Gambäck, B. Named Entity Recognition for Amharic Using Stack‑Based Deep Learning. In Proceedings of the International Conference on Computational Linguistics and Intelligent Text Processing, Budapest, Hungary, 17–23 April 2017; pp. 276–287. Gangula, R.R.R.; Mamidi, R. Resource creation towards automated sentiment analysis in Telugu (a low resource language) and integrating multiple domain sources to enhance sentiment prediction. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan, 7–12 May 2018; pp. 627 – 634. Alemneh, G.N.; Rauber, A.; Atnafu, S. Dictionary Based Amharic Sentiment Lexicon Generation. In Proceedings of the Inter‑ national Conference on Information and Communication Technology for Development for Africa, Bahir Dar, Ethiopia, 22–24 November 2019; pp. 311–326. Gebremeskel, S. Sentiment Mining Model for Opinionated Amharic Texts. Available online: [http://etd.aau.edu.et/handle/1234](http://etd.aau.edu.et/handle/1234) 56789/3029 (accessed on 24 October 2021). Jana, A.; Goyal, P. Can Network Embedding of Distributional Thesaurus Be Combined with Word Vectors for Better Represen‑ tation? In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, New Orleans, LA, USA, 1–6 June 2018; Volume 1, pp. 463–473. [↗](http://etd.aau.edu.et/handle/1234)