# Developing a Hybrid Morphological Analyzer for Low-Resource Languages
## 1 Introduction
Kannada is highly agglutinative in nature, meaning two or more words can be combined into a single word, resulting in a complex word structure. It is morphologically rich in nature by having a greater number of inflections for a given word. The agglutinative nature of the language makes it difficult to split the words conjoined as a feature of euphonic change (_Sandhi_), in which case it is even more difficult to perform morphological analysis [13]. In English, a verb _learn_ undergoes inflection based on person/number and tense as _learnt_, _learning_, _learns_, _learned_, whereas in Kannada, the equivalent word for _learn_ is _kali_, and inflections based on gender, number and tense are shown in Table 1.  
From Table 1, it is clear that there are multiple suffixes added based on gender, person, number, and tense for a verb in Kannada, whereas it is simplified in English as the inflections are unaffected for gender, but change only for person/number and tense. English still undergoes the -s inflection in the third-person singular form. These inflections show the complex structure and morphological richness of the Kannada language, which makes it challenging to develop a morphological analyzer in Kannada. The complex structure of morphemes makes it difficult to build an automated morphological analyzer, as linguistic features such as the case markers, gender, tense, number, and honorifics affect the morpheme, leading to more inflected forms. In Kannada, several researchers have proposed methods for word categorization into declinables and indeclinables [14]. Declinables are nouns, verbs, adjectives, and pronouns, and indeclinables are conjunctions, interjections, and adverbs. Verbs undergo inflection based on gender, number, and tense. Pronouns and adjectives generate inflection similar to that of nouns. Compared to nouns, verbs have more inflections. Indeclinables like conjunctions, interjection, and adverbs do not show any inflection based on gender, number, tense, or case. Using these declinables and by building a dictionary of stems and affixes, it is possible to generate morphological analyses in Kannada. Considering the above-mentioned challenges, a Kannada morphological analyzer can be approached in two ways: the first is a rule-based approach, which utilizes a dictionary and applies predefined morphological rules to the words in the dictionary. This approach involves manually defining the rules, which are subsequently integrated into the system. The second approach is corpus-based, where a corpus is created, and machine learning algorithms are applied to generate the morphological analysis [15]. A paradigm refers to a set of rules that can be recognized in the process of word formation [16]. A morphological analyzer can be built by creating paradigms for various word categories in a dictionary. The rule-based approach is the most suitable approach among analysis techniques for morphologically rich languages, as there can be more inflections generated with multiple suffixes being added to the stem [17]. Deep learning methods have also been popular among NLP researchers for morphological analysis due to the availability of open-source architectures [18]. The researchers have implemented morphological analyzers using sequence-to-sequence, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Transformer architectures. Based on the significance of deep learning techniques in NLP, this paper focuses on developing rule-based paradigms for morphological analysis and building a unique corpus on which a Transformer architecture is trained [19]. The significant outcome here is to generate the morphological analysis of new words that are not present in the dictionary. The following are the contributions in developing a morphological analyzer for the Kannada language:  
1. A set of paradigms is designed for the words in a standard dictionary using morphological rules based on gender, number, tense, person, and case applicable to individual word categories. 2.