## 2 Related Work  
The classical approaches to lemmatizing highly resourced languages are based on two main approaches: (1) the rule-based approach and (2) the dictionary-based approach. Recently, neural network-based approaches have been used, with limited success. Rule-based lemmatizers have been widely focused on by researchers for English and other languages. In [8], researchers proposed a rule-based English lemmatizer using the ripple-down approach. In this approach, suffixes are added and removed from a word to get the root form. Similarly, ref. [9] developed a rule-based lemmatizer for the Hindi language. They introduced rules, based on the structure of the Hindi language, to perform operations on the suffix of an input word to generate the useful lemma of the word. This method extracts the suffix from the given word and adds the characters (generated by rules) at the end of a word. They reported 89.08% accuracy with this lemmatizer. In [10], the authors developed a rule-based lemmatizer for Icelandic. They used suffix substitution rules derived from a database to find the lemma. They experimented with both PoS-taggedtext and non-PoS-tagged text. The non-PoS-tagged text produced the best accuracy score of 99.55%. A neural lemmatizer was developed by [11]. It was developed for the Bengali language. They used a feed-forward neural network with k fold cross-validation approach. This rule-based lemmatizer achieved an accuracy of 67.57%. Similarly, there have been efforts to develop lemmatizers using a dictionary-based approach in recent years. In [12], the authors developed a lemmatizer using a dictionary-based approach. The Lemmatizer mainly dealt with the issue of vocabulary words. It was developed for four languages; Finnish, Swedish, German, and English. The highest \(F_{1}\) score they achieved was 70%. A Turkish lemmatizer was developed in [13]. They used a dictionary-based approach to derive the lemma of a word. They prepared a dictionary of 1,000,000 words and achieved 91.90% accuracy. Similarly, an Arabic lemmatizer was proposed in [14], based on the non-statistical approach. This Lemmatizer was developed for information retrieval tasks. The accuracy of this Lemmatizer was reported as being 89.15%. They used different resources from the Arabic language to build the dictionary and to generate more accurate lemmas. In [15], the researchers developed a multilingual lemmatizer. It was based on the Helsinki finite-state transducer technology, and lemma dictionaries were automatically generated from the proposed approach. They developed this Lemmatizer for the English, German, Dutch, French, and Spanish languages. The authors in [16] proposed a lemmatization and POS tagging model based on a Bi-LSTM architecture to analyze 11 th-century Paleographic Tamil stone inscriptions. The model was designed to accurately classify and predict tags for words, providing 96.43% accuracy. Other research, [17], presented a supervized machine learning approach using a Naive Bayes algorithm to lemmatize the Setswana language, showing that it was more reliable than rule-based approaches. This model shifted from hand-programmed rules and could lemmatize words contextually, based on how they were used. Experiments yielded an accuracy of 70.32% by using a medium-sized dataset for training and testing. The results showed that machine learning is more reliable than rule-based approaches for lemmatizing Setswana words in context. The authors in [18] presented a lemmatization algorithm for the Uzbek language. They used an algorithm that utilized a finite state machine to remove affixes, a database of affixes, and parts of speech knowledge to identify the lemma of a given word. Their model uses a process that consists of general rules, affix classification, affix removal, and definition of the lemma. The algorithm was tested with an Uzbek corpus containing 80,000 words and phrases. Recently, a framework, [19], was proposed for extracting lemmas from inflected Bangla words, considering their parts of speech as context.