# Developing a Hybrid Morphological Analyzer for Low-Resource Languages
## 2 Related Work
### Morphological Analyzer for Dravidian Languages  
[PERSON] et al. developed a morphological analyzer for the Tamil language using a sequence labeling approach of machine learning [23]. Support Vector Machine was used in morphological analysis for classification. Their work employs machine learning techniques in data preparation without a morpheme dictionary, but the system is trained on morpheme boundaries. [PERSON] et al. surveyed three major methods for developing a morphological analyzer in Malayalam, viz. paradigm-based and suffix-stripping hybrid [24] methods. The availability of morphological paradigms and classification was a major issue in developing a morphological analyzer.  
[PERSON] and [PERSON] proposed an analyzer only for Kannada verbs [25]. They created verb, suffix, and root databases and used a hybrid approach with suffix-stripping for the paradigms. Although they used transliterated text, there was no proper standardized notation mentioned in their model. [PERSON] et al. proposed a rule-based stemmer, an analyzer, and a generator for nouns in the Kannada language [26]. Their model made use of noun-suffix- and noun-noun-based dictionaries, which were implemented using suffix stripping. Their work was restricted to only nouns, whereas other parts of speech (POSs) present in the Kannada language were ignored. [PERSON] et al. proposed a rule-based morphological analyzer and generator using Finite State Transducer (FST) [15]. The system was implemented using the AT&T Finite State Machine. The lexicon data and the analyzer are not openly available, and the testing was performed on random samples.  
[PERSON] et al. proposed a morphological analyzer for Kannada using a hybrid approach [27]. The affixes are stripped, followed by the use of a paradigm-based approach. A rule-based method is employed in the creation of the lexicon. A questionnaire-based approach was used to make new entries into the lexicon. Kannada's derivational words and foreign words were not considered for nouns, and for verbs, multiple suffixes were not considered. [PERSON] et al. proposed a morphological analyzer and generator for Kannada using the _Trie_ data structure in a paradigm-based approach [28]. They constructed individual _tries_ for handling suffixes corresponding to each paradigm class and linked them to the roots that could be mapped to the same paradigm. A limitation of their work was the high memory consumption.  
[PERSON] proposed a network and process model for a morphological analyzer and generator [29]. In this approach, the Sandhi (euphonic change) between the root and affixes was treated as a process, while the formation of morphemes was treated as a network modeled using a finite automaton, following a rule-based approach. Limitations of rule-based approaches lie in their inability to span the rules on new words that were not seen as part of the lexicon. [PERSON] et al. proposed a Morph-analyzer for the Kannada dialect [30] by employing machine learning techniques like Support Vector Machine on printed Kannada text. They extracted the words by OCR and performed morphological analysis. A custom dataset was developed using a Romanized Kannada text, without any standard format, which is not publicly available. There was no explicit algorithm provided for handling grammatical features or for performing morphological analysis.  
Most of the methods employed for Dravidian languages were rule-based in nature by making use of a dictionary. The creation of paradigms poses a challenging task for this kind of morphological analysis. As language evolves and new words are added to the dictionary, rule-based methods may struggle to generate accurate analysis, subsequently leading to failures in adapting to these changes.