Context Aware Lemmatization and Morphological Tagging Method in Turkish

TL;DR

This paper introduces a Turkish lemmatization and morphological tagging model that incorporates word meaning, utilizing bidirectional LSTM and Turkish BERT, achieving superior results compared to existing models.

Contribution

First to develop a meaning-sensitive lemmatization and tagging model for Turkish using BERT and LSTM, advancing linguistic processing in Turkish NLP.

Findings

Models outperform SIGMORPHON 2019 results.

Incorporating meaning improves lemmatization accuracy.

Bidirectional LSTM and Turkish BERT effectively model Turkish morphology.

Abstract

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.