The smallest part of a word that defines the word is called a word root. Word roots are used to increase success in many applications since they simplify the word. In this study, the lemmatization model, which is a word root finding method, and the morphological tagging model, which predicts the grammatical knowledge of the word, are presented. The presented model was developed for Turkish, and both models make predictions by taking the meaning of the word into account. In the literature, there is no lemmatization study that is sensitive to word meaning in Turkish. For this reason, the present study shares the model and the results obtained from the model on Turkish lemmatization for the first time in the literature. In the present study, in the lemmatization and morphological tagging models, bidirectional LSTM is used for the spelling of words, and the Turkish BERT model is used for the meaning of words. The models are trained using the IMST and PUD datasets from Universal Dependencies. The results from the training of the models were compared with the results from the SIGMORPHON 2019 competition. The results of the comparisons revealed that our models were superior.

本研究旨在填补土耳其语词根化研究中对词义敏感性缺失的空白，提出了一种结合双向LSTM和土耳其BERT模型的创新方法，能够同时进行词根识别和语法标记。研究结果表明，该模型在准确性上超越了SIGMORPHON 2019竞赛的结果，对土耳其语自然语言处理领域具有重要影响。

土耳其语中的上下文敏感词根化和形态标记方法