## 2 Related Work on Diacritics Restoration
### Classical Approaches
#### 2.1.4 Character-Level Approaches  
Another important direction in diacritics restoration is character-level approaches. They solve problems that are typically defined as sequence labeling. The iterative process slides through an undiacritized sequence of characters by assigning their diacritized equivalents (labels). Each character is a separate classification instance with the surrounding content as other classification features. Such approaches typically require no additional language tools except for the raw text, which makes them suitable for less-resourced languages. Moreover, character-level methods are robust when dealing with unknown words. Depending on the chosen classifier, this classification process can be viewed as the independent instance-based classification (assuming that each instance is independent) or the sequence classification (considering conditional dependencies between predictions) problems. The seminal research work in [25] described the instance-based classification technique applied to the Czech, Hungarian, Polish, and Romanian languages. Authors tested different window sizes (of \(1,3,5,7\), and \(9\) lower-cased characters to both sides) with two classifiers:the memory-based approach and the decision tree (C4.5). Their offered method achieved an accuracy which is competitive to word-level approaches. Another study, presented in [26], described the sequence classification tackled with the MaxEnt classifier. This approach is applied to the Arabic language, but instead of pure character features, it employs character- (character \(n\)-grams), segment- (words decomposed into prefixes, suffixes, stems, etc. ), and part-of-speech tag-based feature types. The successful combination of these diverse sources resulted in a high diacritization accuracy. Similar to [25], three instance-based classifiers (a decision tree, logistic regression, and the Support Vector Machine, or SVM), with character \(n\)-grams (from a sliding window) as features, were investigated for the Hungarian language [27]. The decision tree, which is also good at identifying important features and keeping decisions easy to interpret, was determined to be the most accurate. This research is important for several reasons: it claims the effectiveness of the offered approach on non-normative language (web data, Facebook posts) and the superiority over lexicon lookup (retrieving the most common diacritized forms) and hybrid (the lexicon plus character bigrams) approaches in the comparative experiments. However, comparative experiments are not always in favor of character-level approaches. In [28], the character-level and word-level approaches are compared for the Lithuanian language. The authors used conditional random fields (CRF) as the sequence classifier by applying them to the character-level features. Despite different window sizes (up to 6), the character-based approach was not able to outperform the trigram language model with the back-off strategy. The character-based approach was also not the best choice when applied to the Spanish texts [29]. It was outperformed by the decision list (that combines the simple word-form frequency, morphological regularity, and the collocational information) and the part-of-speech tagging (trained on the tagged corpus with information about the diacritic placement) approaches. Two approaches, namely, sequence labeling (i.e., sequence classification) and SMT were compared in [30] for the Tunisian language. The sequence classification approach uses CRF as the classifier and is applied to the different character (windows up to 6-grams) and word-level (part-of-speech tags of two neighboring words) features. The SMT approach uses Moses with a 5-gramlanguage model and other parameters set to their default values. The comparative experiments demonstrated the superiority of the sequence labeling approach compared to the SMT approach.