In the realm of Natural Language Processing (NLP), understanding the nuances of language is essential for tasks ranging from language generation to sentiment analysis. This foundation rests upon two pillars: Syntax and Semantics. Together, they provide the framework for comprehending and generating human language. SYNTAX Syntax is the structural backbone of…

This article explores the mathematization of meaning and context in NLP through distributional semantics and contextualized word representations. — In this article, we uncover various methods used to ascertain the similarity between words and sentences, including lexical similarity, semantic similarity, syntactic similarity, machine learning methods, and hybrid approaches. However, our main focus lies on distributional similarity, a technique that analyzes the contextual patterns in which words or sentences appear.

In this article, we explore the mechanics of attention, encoder-decoder dynamics, and positional encodings, unraveling the essence of how these elements unite to elevate the capabilities of language models. — 1. INTRODUCTION Transformers break the text into parts, use attention to focus on important information, and generate meaningful output using the encoder and decoder. Imagine you’re part of a study group, and you need to work together to understand a complex article. Instead of discussing it out loud, you decide to write…

In NLP, understanding the intricate relationships between words and context is a challenge. By harnessing the power of word embeddings, we can unlock semantic connections and enhance NLP models. This article presents an overview of the same. — It is advisable to read about Bag-ofWords and TF-IDF before going into Word Embeddings. here are the links: NLP: Explain Bag of Words. The Bag of Words (BoW) model is a simple and was widely used technique in natural language processing (NLP) for…aaweg-i.medium.com

The Bag of Words (BoW) model is a simple and was widely used technique in natural language processing (NLP) for representing textual data. It treats a document as a collection of words, disregarding grammar, word order, and context. …

Convert words into numbers — It is advisable to go through basics og Bag of Words before delving into TF-IDF: NLP: Explain Bag of Words. The Bag of Words (BoW) model is a simple and was widely used technique in natural language processing (NLP) for…aaweg-i.medium.com TF-IDF stands for Term Frequency-Inverse Document Frequency. It is a numerical representation technique commonly used in natural language processing (NLP) to reflect the importance of a word in a document within a collection or corpus.

This article explores various aspects of natural language processing (NLP). The evolution of machine learning techniques, the significance of transformer architectures, and the role of attention mechanisms are highlighted. The article emphasizes the importance of data labeling, tokenization, and Vectorization. Then after a non-mathematical of Transformers, it goes into emergence of pre-trained models and the role of Hugging Face as a platform for accessing and utilizing these models effectively.