## 4 Discussion
We could not compare the results for several reasons such as (1) errors that occurred during translation and (2) the ground truth annotation scores are directly taken from the English dataset, which might not be optimal. In the future, we suggest to re-annotate the datasets using Amharic experts. We have also observed that the Simlex999 datasets are challenging for the similarity computation task. The \"fastText\" model achieves better results compared to the other Amharic semantic models. Pre-trained models achieve the lowest results as we can witness from the lower standard deviation (\(\sigma\)) scores. * **POS tagging**: As seen in Table 6, the semantic models from the transformer-based embeddings perform as good as the CRF algorithm for the POS tagging task. While training the deep learning models took much longer than the CRF algorithm, using deep learning models avoids the need to identify features manually. Both AmRoBERTa and CRF-based models predict the conjunctions, interjections, and prepositions correctly. However, for the rare tags (that occurs fewer times), such as ADJPCS (Adjective with prep. & conj. singular) and VPSs (Verb with prep. singular), AmRoBERTa predicts the  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & \(\mathsf{n\&\alpha(\text{ox})}\) & \(\mathbf{\texttt{  
nearest popular tag. However it was observed that CRF perfectly memorizes the correct tag. For example, the word **h, \(\gamma\)K\(\gamma\)F7Y**, which should be a spelling error (maybe the last \(s\) stands for spelling error inVPSs ), is tagged as VPS with AmRoBERTa. We have also observed that the FLAIR contextual embeddings perform very well compared to the network embedding models. The new Amharic FLAIR embeddings (**AmFlair**) and the fine-tuned models (**MultFlair**FT) produce a slightly better result than the publicly-available multilingual FLAIR (**MultFlair** embeddings). * flower), from verbs (**M\(\delta\)**-**
- shined), and from adjectives (**J-H**
* **Sentiment analysis**: For the sentiment analysis task, we have observed that the deep learning approach outperforms the different classical supervised classifiers. Unlike the NER and the POS tagging tasks, the deep learning approach based on the network embeddings, specifically the **Role2 Vec** approach outperforms the other models. Based on our error analysis, we found out that sentiment analysis is challenging both for users and machines as the meaning of the tweet depends on a specific context. Moreover, metaphorical speech and sarcasm are very common in Amharic text, especially on the Twitter dataset, which makes automatic classification very difficult. In general, we can see that the different semantic models impact various tasks. One semantic model will not fit the need of multiple NLP applications. Another observation is that fine-tuning models or building models with a corpus that is carefully crafted have a better impact on the specific tasks. We believe that the models we publish will help in the development of different NLP applications. It will also open a different research direction to conduct more advanced research as well as to carry out insightful analysis in the usage of semantic models for Amharic NLP.