## 2 Related Work
Disambiguation involves using different combinations of these lexical rules to detect the pattern. In another work, the authors explored a rule-based approach to MD, integrating a set of predefined constraint rules with an algorithm that automatically learns additional rules. It is denoted as constraint-based MD [15]. Following this constraint-based MD, a pure rules-based approach [16] was proposed for Turkish MD. The approach is similar to that described in [15], since all the rules are designed and chosen manually; the disambiguator uses more capable and descriptive formatting for the disambiguation rules. Statistically based approaches have been proposed for extracting the lexical pattern of words to disambiguate the morphology ambiguity. In this direction, hidden Markov model (HMM)-based approaches [2] were proposed for MD by modeling the transition and emission probabilities from features of the observations. To avoid the complex structure of the MD label, the authors break down each analysis into smaller units called inflectional groups (IGs). Then, HMM is used to calculate the transition and emission probabilities between these inflectional groups. This approach makes assumptions that the current word prediction only depends on the previous words (tri-grams); in MD, it is used between IGs. For instance, in a model, the presence of IGs in a word only depends on the final IGs of the previous words. The IGs-based model for Turkish achieves 92.08% accuracy, while the root-based model achieves 80.36%. The combined model improves the accuracy further, achieving 93.95%. In [17], the authors proposed an open source toolkit for Turkish text processing including a morphological analyzer and disambiguation, in which they tried different methods of disambiguation as follows: (i) note-learning disambiguator: this counts analyses observed in the training data; for testing, it picks the analysis with the highest frequency as correct; (ii) model without root: instead of performing a complete analysis, it decomposes the analysis into two parts--root \(r\) and a sequence of morphological tags \(a\). Then, they formulate the joint probability to \(P(r,a)=p(r|a)p(a)\); (iii) model with IGs: instead of decomposing the analysis into two parts, in this model, it is split into IGs, and the joint probabilities between these IGs are modeled. Experimental results showed that the last two models achieved comparable accuracy on a test set similar to the training set (the text domain is news). The final model demonstrated better accuracy on a smaller test set that differed more significantly from the training set. It also showed that the models performed worse when the boundaries of the IGs or tags were not clear. In [18], a set of classifiers was proposed for MD; using the J48 Tree algorithm, a highest accuracy of 95.61% was obtained. Using a trigram sequence, a perceptron approach was proposed with 23 features [3], and using these features, the results of accuracy were improved from 93.95% to 96.80% in MD. In [19], a bidirectional long short-term memory network-based neural network architecture was introduced for disambiguating morphological parses using different amounts of contextual information. The results demonstrated that the type and amount of context required for effective disambiguation vary across languages, depending on their linguistic characteristics. In languages like Turkish, where morphological information is largely conveyed by the surrounding context, models utilizing surface context can effectively capture long-range dependencies to resolve ambiguities. In contrast, languages like Arabic, where surface representations are less informative, there is a significant benefit from incorporating representations of surrounding parse candidates alongside the surface forms of neighboring words. MD is treated as a Seq2 Seq problem [20; 21; 22]. It takes the characters of a word as an input sequence, and for each word, generates a morphological analysis. Each analysis can be decomposed into a sequence of tags.