Exploring Word-Level Representations in Modern Natural Language Processing

Abstract

Abstract: Natural Language Processing (NLP) is a dynamic and rapidly advancing field at the intersection of artificial intelligence and linguistics, focused on enabling computers to understand, process, and generate human language. Recent advancements intransformer-based models have significantly improved NLP capabilities, enablingmachines to understand and generate human language more effectively. This paper provides a comprehensive overview of NLP, tracing its historical development and recent trends. The discussion includes the different phases of NLP, from text pre- processing and tokenization to syntactic and semantic analysis, along with pragmatic considerations. Text normalization techniques, such as stemming, lemmatization, and removing stopwords, are explored to emphasize their importance in preparing raw text for analysis. Additionally, this paper presents a comparative analysis of popular word-level representation techniques used in NLP, including One-Hot Encoding, Bag of Words (BoW), and Term Frequency-Inverse Document Frequency (TF-IDF).