Enhancing Korean Named Entity Recognition With Linguistic Tokenization Strategies

Abstract

Tokenization is a significant primary step for the training of the Pre-trained Language Model (PLM), which alleviates the challenging Out-of-Vocabulary problem in the area of Natural Language Processing. As tokenization strategies can change linguistic understanding, it is essential to consider the composition of input features based on the characteristics of the language for model performance. This study answers the question of Which tokenization strategy enhances the characteristics of the Korean language for the Named Entity Recognition (NER) task based on a language model? focusing on tokenization, which significantly affects the quality of input features. We present two significant challenges for the NER task with the agglutinative characteristics in the Korean language. Next, we quantitatively and qualitatively analyze the coping process of each tokenization strategy for these challenges. By adopting various linguistic segmentation such as morpheme, syllable and subcharacter, we demonstrate the effectiveness and prove the performance between PLMs based on each tokenization strategy. We validate that the most consistent strategy for the challenges of the Korean language is a syllable based on Sentencepiece.