## 4 Discussion  
In this section, we will briefly discuss the effects of the different semantic models for the respective NLP tasks. * **Top \(n\) similar words**: For the top \(n\) similar words experiment, as seen from Table 3, the fastText model seems to produce irrelevant suggestions compared to the AmWord2 Vec model, which is associated with the smaller corpus size used to train fastText. If we search the predicted word provided by fastText on the Amharic Wikipedia page, we can see that the word co-occurs with the target word only on one occasion. However, as we did not train a new fastText model from scratch using our dataset, we can not claim if the suggestions are mainly due to the size of the dataset or due to the training approach used in fastText architecture. The similar words suggested by AmDT, AmWord2 Vec, and DeepWalk are comparable (see Table 3 and 9). In general, the suggestions from AmDT are more fitting than the ones generated using AmWord2 Vec, but the suggestions from AmDT and DeepWalk are equally prevalent to the target words. This corresponds to the finding that the conversion of DTs to a low-level representation can easily be integrated into different applications that rely on word embeddings [61]. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Model** & **Precision** & **Recall** & **F1** \\ \hline \multicolumn{4}{c}{TF-IDF representation} \\ \hline LogReg & 46.80 & 60.88 & 52.92 \\ RanfomF & 44.59 & 52.17 & 48.09 \\ KNN & 49.85 & 50.22 & 50.03 \\ NearestC & 47.36 & 49.20 & 48.26 \\ SVM & 35.44 & 46.42 & 40.20 \\ \hline \multicolumn{4}{c}{Word Embeddings} \\ \hline AmWord2 Vec & 55.54 & 54.91 & 55.22 \\ fastText & 43.50 & 67.81 & 53.00 \\ \hline \multicolumn{4}{c}{Contextual Embeddings} \\ \hline AmFlair & 53.24 & 59.25 & 56.09 \\ MultFlair & 46.96 & 55.05 & 50.68 \\ MultFlairFT & 54.49 & 59.58 & 56.92 \\ AmRoBERTa & 46.62 & 56.39 & 51.04 \\ \hline \multicolumn{4}{c}{Graph Embeddings} \\ \hline DeepWalk & 55.89 & 57.71 & 56.78 \\ Role2 Vec & **56.26** & **60.89** & **58.48** \\ \hline \multicolumn{4}{c}{Baselines} \\ \hline Stratified & 33.79 & 33.80 & 33.80 \\ Uniform & 29.72 & 30.96 & 30.33 \\ MostFreq & 33.33 & 17.31 & 22.78 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Experimental results of the test sets on the sentiment classes: “Positive”, “Negative” and “Neutral” (macro-averages). However, the similar words predicted by the DT and word2 Vec embeddings are static and it is up to the downstream application to discern the correct word that fits the context. However the next word prediction using the transformer-based models, in this case from the AmRoBERTa model, predicts words that can fit the context. From Examples 1 and 2, we can see that the \"masked\" words are to be predicted in the two sentences. The two sentences are extracted from the online Amharic news channel ([[https://www.ethiopianreporter.com/](https://www.ethiopianreporter.com/)]([https://www.ethiopianreporter.com/](https://www.ethiopianreporter.com/)) (accessed on 24 October 2021)) and we masked the word **?C&** in both sentences. In the first sentence (S1), it refers to \"profit\" while in the second sentence (S2), it intends \"additional\" or \"more\". We can see from Table 4 that the AmRoBERTa model generated words that can fit the context of the sentence. We have also observed that AmRoBERTa helps in word completions tasks, which is particularly important for languages such as Amharic, as it is morphologically complex. * **Word similarity/relatedness**: For the WordSim353 word pair similarity/relatedness experiment, the \"DeepWalk\" and \"AmWord2 Vec\" models produce the best results. While this is the first dataset and experiment for the Amharic, the [PERSON]'s correlation (\(\rho\)) result is better than most of the knowledge-based results for the English counterpart datasets. The state-of-the-art result for English reaches a score of 0.828, which is much larger than the results for Amharic scores.