## 2 Related Work  
A morphological analyzer generates a set of morphological analyses for an input word, and the MD task performs morphological disambiguation by choosing a possible analysis among the candidates depending on the context. Morphological disambiguation has been studied extensively over the past decades, especially for agglutinative languages like Turkish and Kazakh. Approaches for morphological ambiguity resolution can be categorized into three problem groups as follows: (i) sequence labeling problem; (ii) morphological disambiguation problem; (iii) sequence-to-sequence (Seq2 Seq) problem. Sequence labeling is a type of problem in NLP where the goal is to assign a categorical label to each token in a sequence of tokens. This type of problem is essential in various NLP tasks, such as POS tagging [10] and named entity recognition (NER) [11]. In a sequence labeling problem, an input sequence \(X=(x_{1},x_{2},\ldots,x_{n})\) of length \(n\) is given, and the task is to predict a corresponding sequence of labels \(Y=(y_{1},y_{2},\ldots,y_{n})\). Each label \(y_{i}\) corresponds to an input element \(x_{i}\), and the labels are drawn from a predefined set of categories. MD is treated as a sequence labeling problem, similar to POS tagging. However, MD is more complex than POS tagging because its labels include the root, POS tag, and a sequence of morpheme tags. There are two ways of performing MD as sequence labeling: (i) treat each morphological analysis as a label; usually they are drawn from the training as a predefined set of categories. Since a morphological analysis contains the root, POS tag, and a sequence of morpheme tags, it results in a large number of unique labels. It not only increases label data sparsity but also leads to a potential issue of out-of-vocabulary for a label. (ii) To avoid this issue, most of the sequence labeling approaches for MD use a multi-class and multi-label model to predict different morphological categories. For each morphological category, there is a separate classifier. In [12], the authors proposed different models with different architectures for morphological tagging. In their multiclass, multilabel (McMI) model, which predicts POS, different morphological categories are employed separately as the output of the model; however, they share an input layer. Since the morphological tagging contains a tag sensitivity issue (one tag may depend on previous tags), to capture this information, the authors proposed a hierarchical multi-class, multilabel model (HMcMI); in this architecture, the authors only consider the POS tags' sensitivity. Another approach was the testing of a sequence model (Seq); this takes each word in a sentence, and feeds it to a long short-term memory LSTM [13]) network as a context vector; then, for the decoder, it generates category-value pairs. The authors take a multiclass model (MC) as their baseline, treating an analysis as a label. The experiments were conducted on UDv2.1 corpora for 49 languages. The experiments showed that, on average, the Seq model performed the best compared to the others. For the OOV case, no significant difference was observed between the HMcMI and McMI models. For large datasets, it seems that the baseline MC outperformed these two models. For POS tagging, HMcMI outperformed McMI. This type of approach is efficient for languages with less complex morphology, such as English and others; it treats the problem as a morphological tagging problem, which essentially involves predicting the entire set of morphological tags given the context. In contrast to predicting the entire analysis given a word context, treating it as a disambiguation problem uses all candidates and selects the most probable analysis. In this direction, approaches to the MD task for Turkish began with a rule-based method reported in [14]. It uses a constrained lexical rule to select the most probable analysis among the candidates. The lexical rule contains the word case, POS tags, and the positional features of the word.