Paper Contents
Abstract
This research paper explores the efficacy of subword tokenization in enhancing contextual understanding and performance in Natural Language Processing (NLP) models, specifically ELMo and BERT. Subword tokenization breaks words into smaller units, capturing morphological variations and handling out-of-vocabulary (OOV) words, making the models more robust to diverse word forms. By feeding resulting token sequences into ELMo and BERT, we demonstrate their ability to recognize similarity between words, even with limited occurrences in the training data. The modelscontextual embeddings capture fine-grained language patterns, leading to improved performance on various NLP tasks. Experimental results on sample sentences highlight the effectiveness of subword tokenization in enabling better context comprehension and overall performance enhancement in ELMo and BERT, advancing the field of NLP research.
Copyright
Copyright © 2023 Aatmaj Amol Salunke. This is an open access article distributed under the Creative Commons Attribution License.
