# Developing a Hybrid Morphological Analyzer for Low-Resource Languages
## 1 Introduction  
Natural Language Processing (NLP) plays a key role in developing speech and text processing systems, wherein the analysis of words and their formation is an essential component. Morphology is the systematic study of word formation, where details of a language are required in the process of constructing a meaningful sentence. Morphological analysis is an essential phase in NLP where a morphological analyzer decodes a given word into its constituent components: stem and affixes [1]. Given a word, its morphological analysis can be generated by humans who might have learned the language formally or informally or by machines trained to use a morphological analyzer. A morphological analyzer can serve as a necessary component in developing Stemmer, Named Entity Recognition (NER), and Machine Translation (MT) systems, to name a few NLP applications [2]. The morphological analyzer generates morphemes for a given word, where a morpheme is a minimal meaningful unit, which can be either a stem or an affix, causing inflections based on number, gender, tense, or case [1]. The inflections are based on morphological rules that vary from one language to another and are often associated withparts of speech that contribute differently during inflections. For instance, verbs in English have inflections with the _-ing or -ed_ suffix being added to the stem [3]. The various parts of speech contribute differently in inflections. Nouns undergo inflection based on the eight cases: nominative, accusative, instrumental, dative, ablative, genitive, locative, and vocative, whereas verbs inflect based on tenses: past, present, and future. Indecinables do not generate inflections when gender, tense, number, or case are altered [4]. Automated morphological analysis can assist in downstream NLP applications, especially for low-resource languages and assist language documentation efforts for endangered languages [5]. Many low-resource languages lack large, annotated corpora or linguistic resources, making it difficult to analyze or process them computationally. By developing morphological analyzers, it becomes possible to create linguistic resources for such languages. This supports machine translation and contributes to the documentation and preservation of endangered languages. The purpose of the study is to encourage the development of morphological analyzer for low-resource languages, whose development can help build more NLP tools. In machine translation, especially for morphologically rich languages, morphological analyzers play a crucial role in enhancing translation accuracy by breaking down complex words into constituent morphemes. This approach addresses challenges such as data sparsity and vocabulary explosion, which are prevalent in languages with intricate morphological structures [6]. Morphological analysis is particularly beneficial for low-resource languages, where large parallel corpora are scarce. By segmenting words into meaningful units, translation systems can better handle rare and out-of-vocabulary words, improving translation quality [7]. In recent decades, morphological analyzers have been built for English and other European languages. The Stanford CoreNLP [8] toolkit processes mainly the English language but also supports languages like French, German, Arabic and Italian. Morphological analyzers have been built for Indian languages [9] by creating the necessary corpus or dictionary. Indian languages are broadly classified as belonging to the following families: Indo-European (Hindi, Marathi, Urdu, Gujarati, and Sanskrit), Dravidian (Kannada, Tulu, Tamil, Telugu, Malayalam), Austroasiatic (Munda in particular), and Sino-Tibetan (Tibetou-Burman in particular) [10]. Kannada is one of the Dravidian languages spoken widely in the state of Karnataka. It is included in the eighth schedule of the Indian Constitution [11] and has approximately 40 million speakers in India. In this paper, the WX notation is used throughout to represent Kannada words [12].