Token Encodings in Natural Language Processing (NLP)

Introduction

Natural Language Processing (NLP) is a rapidly advancing field that deals with the interaction between computers and human language. NLP applications range from sentiment analysis and language translation to chatbots and virtual assistants. A fundamental aspect of NLP is the representation of text data in a format that machine learning models can understand and process. Token encodings, also known as word embeddings or word representations, play a pivotal role in transforming human-readable text into numerical representations that can be effectively utilized by NLP algorithms. In this essay, we will explore the concept of token encodings in NLP, their importance, and various techniques used for token encoding.

Foundations

Token encodings serve as the bridge between natural language and machine learning models. They are essential for transforming raw text data into fixed-dimensional numerical vectors, enabling efficient computation and analysis. In traditional NLP approaches, text data was represented using sparse one-hot encodings, where each word in the vocabulary was associated with a unique binary vector. However, these one-hot representations suffer from high dimensionality and lack the ability to capture semantic relationships between words. As a result, they are not ideal for complex NLP tasks that require a deeper understanding of language.