# Developing a Hybrid Morphological Analyzer for Low-Resource Languages
## 3 Methodology
### Dataset Creation
#### 3.1.3 Paradigm Generation
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Sentences** & **P (500)** & **R (500)** & **F1 (500)** & **P (5000)** & **R (5000)** & **F1 (5000)** & **P (10,000)** & **R (10,000)** & **F1 (10,000)** \\ \hline
100 & 0.921 & 0.921 & 0.921 & 0.926 & 0.926 & 0.926 & 0.931 & 0.931 & 0.931 \\
200 & 0.915 & 0.915 & 0.915 & 0.928 & 0.928 & 0.928 & 0.928 & 0.928 \\
300 & 0.919 & 0.919 & 0.919 & 0.929 & 0.929 & 0.929 & 0.93 & 0.93 & 0.93 \\
400 & 0.914 & 0.914 & 0.914 & 0.926 & 0.926 & 0.926 & 0.926 & 0.926 & 0.926 \\
500 & 0.917 & 0.917 & 0.917 & 0.927 & 0.927 & 0.927 & 0.928 & 0.928 & 0.928 \\
600 & 0.916 & 0.916 & 0.916 & 0.928 & 0.928 & 0.928 & 0.928 & 0.928 & 0.928 \\
700 & 0.918 & 0.918 & 0.918 & 0.929 & 0.929 & 0.929 & 0.929 & 0.929 & 0.929 \\
800 & 0.918 & 0.918 & 0.918 & 0.929 & 0.929 & 0.929 & 0.929 & 0.929 & 0.929 \\
900 & 0.913 & 0.913 & 0.913 & 0.924 & 0.924 & 0.924 & 0.925 & 0.925 & 0.925 \\
1000 & 0.913 & 0.914 & 0.913 & 0.924 & 0.925 & 0.924 & 0.924 & 0.925 & 0.925 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Evaluation scores for the generated morphological analysis of nominal inflections by varying the sentence count (P-Precision, R-Recall, F1-F1 Score P (5000) indicates precision at 5000 th step of training). The perplexity score indicates the predictiveness of the next word. In Figure 12, the perplexity score at every 500 th step is plotted, and it can be observed that a stable value of 1.11 is achieved between 2500 and 8000 th steps. Later there was a rise to 1.15 before dropping to 1.11 at the 10,000 th step. The proposed model is compared with existing Kannada morphological analyzers and summarized in Table 16.

The existing approaches discussed in the literature show that the majority of methods used in developing a morphological analyzer for Kannada are either rule- or learning-based. Considering a smaller subset of nouns or verbs that were rule-based made the morphological analyzer less useful in practical scenarios, as unknown words could not be categorized. The lack of standard dictionary was a major drawback in the litera,ture which was overcome by the proposed morphological analyzer model. As language evolves, new words continue to be included in the dictionary, but the provision to handle such out-of-vocabulary words is not handled in the literature, whereas the proposed model is capable of handling new nouns and is also based on standard dictionary data that are reliable and can be expanded when new words are added to the dictionary. It can be observed that the proposed hybrid method is suitable for generating an analysis of inflected Kannada

Figure 11: The graphical representation of the overall score for the generated morphological analysis of nominal inflections. Figure 12: A graphical representation of the perplexity score for the generated morphological analysis of nominal inflections. words. The proposed method can be easily applied to any low-resource language to create a dataset as well as to generate a morphological analysis. The morphological analyzer developed for Kannada needed a strong open-source Kannada dictionary containing the majority of words used in everyday vocabulary. The creation of paradigms was an uphill task and needed expert supervision prior to the implementation stage. Kannada, a morphologically rich language, poses challenges while generating inflections based on linguistic features like gender, number, tense, and case. Verbs were harder to analyze than nouns, as verbs yielded a greater number of inflections. The exceptional cases required smart handling as they could be mis-categorized. In the

neural part, the training would be based on the data curated, so cleaning and transformation were crucial. ## 6 Conclusions

A hybrid morphological analyzer leveraging the power of rule-based and Transformer-based architectures has been built for a low-resource language, Kannada.