JOINT MORPHOLOGICAL DISAMBIGUATION AND POS TAGGING FOR AGGLUTINATIVE LANGUAGES

Abstract

Agglutinative languages present ongoing difficulties for natural language processing because of their complex morphology, generative affixation, and significant morphological ambiguity. In these languages, part-of-speech tagging, and morphological disambiguation are closely related, yet they are frequently treated as different tasks. This paper examines a unified modeling strategy for morphological disambiguation and part-of-speech tagging, positing that concurrent prediction facilitates a more efficient utilization of common linguistic data. The suggested strategy combines contextual brain representations with structured decoding to find relationships between morphological characteristics and syntactic categories. Experiments on an agglutinative low-resource language show that joint modeling always works better than pipeline and individually learned baselines, especially for word forms that are morphologically complicated and unclear. Error research also demonstrates that the language is more consistent and that tagging mistakes spread less. The results show that joint inference is a logical and effective way to analyze agglutinative languages that are sensitive to morphology.