Gujarati Language Model: Word Sense Disambiguation using Supervised Technique

Word Sense Disambiguation (WSD) is a complex problem as it entirely depends on the language convolutions. Gujarati language is a multifaceted language which has so many variations. In this paper, the debate has advanced two methodologies for WSD: knowledge-based and deep learning approach. Accordingly, the Deep learning approach is found to perform even better one of its shortcoming is the essential of colossal data sources without which getting ready is near incomprehensible. On the other hand, uses data sources to pick the implications of words in a particular setting. Provided with that, deep learning approaches appear to be more suitable to manage word sense disambiguation; however, the process will always be challenging given the ambiguity of natural languages