Challenges in Natural Language Processing: Overcoming the Complexities of Human Language

Introduction

In recent years, Natural Language Processing (NLP) has witnessed significant advancements, which can be attributed to the development of big data and the increase in computational capacity. Nevertheless, in spite of these technological advancements, the path toward truly intelligent language models continues to be riddled with obstacles. Natural language processing (NLP) systems face considerable challenges in overcoming the complexity of human language, which includes its ambiguity, contextuality, and diversity. To effectively implement natural language processing (NLP) solutions, one must have a profound understanding of both the linguistic complexities and the computing restrictions involved. This is true whether one is attempting to comprehend the subtleties of a single word or to keep the context intact during lengthy talks.

The purpose of this essay is to investigate the significant obstacles that continue to hamper the development of natural language processing (NLP), despite the advantages that large data and contemporary computer technology offer. A wide range of hurdles are included in this category, including ambiguity in language and the inability to comprehend the context, bias in data, and problems with scalability. Moreover, we will talk…