Mapping Morphological Patterns: A Framework for Rinconada Bikol Language Morphological Analysis and Stemming

Abstract

Natural Language Processing (NLP), a subfield of Artificial Intelligence (Al), has gained traction in management research, particularly linguistics. However, only High-resource language is being established in NLP. This paper aims to analyze morphological patterns of Low-resource languages with limited linguistic data and resources available for NLP tasks such as Rinconada Bikol Language (RBL). This paper proposed a framework suited for RBL as the approach to developing the RBL Morphological Analyzer. This paper utilized the framework and evaluated it using Morphological Accuracy, revealing an impressive 90% accuracy in identifying correct analysis and stemming. The system's precision stands at 0.90, with a perfect recall of 1.00, resulting in an Fl score of 0.95. This high level of performance indicates the system's strong ability to recognize morphological features and patterns within the dataset effectively. The findings also reveal that the framework could also accurately analyze the morphological structure of RBL sentences.