ML Mastery

N-Gram Models: The Basics That Kickstart Your NLP Journey Toward LLMs N-Gram Models: The Basics That Kickstart Your NLP Journey Toward LLMs N-gram models are fundamental building blocks in natural language processing that capture the sequential nature of human language. Unlike simpler approaches that ignore word order, N-grams preserve local context and help us model how language flows naturally from one word to the next. What Are N-gram Models? An N-gram model is a probabilistic language model based on the Markov chain assumption, which states that the probability of a word depends only on the previous N-1 words. The "N" in N-gram refers to the number of consecutive words grouped together as a single unit. Types of N-grams N-gram Type Description Example (from "data science is fascinating") Unigram Single word units "data", "science", "is", "fascinating...

Tokenization: How AI Understands Language Tokenization: How AI Understands Language The fundamental process that enables AI systems to comprehend and generate human language These days, we're all interacting with generative AI models like ChatGPT and Gemini. But have you ever wondered how these systems actually understand your questions and generate relevant responses? The answer lies in a sophisticated process that transforms human language into a format that machines can comprehend and manipulate. While massive training datasets containing billions of words are crucial for AI performance, the secret lies in how we prepare this data for AI systems. Raw text, as humans write it, is messy and inconsistent. It contains spaces, punctuation, capitalization variations, and countless linguistic nuances that machines struggle to process directly. This brings us to the core concept that bridges human lang...

Understanding Morphology in NLP: Morphemes, Stemming, Lemmatization & Lexicon Explained Morphology in NLP: Breaking Words to Make Machines Smarter A Deep Dive by Sasi | Explaining Morphemes, Stemming, Lemmatization, and Lexicons Hey everyone 👋! Let’s dive deeper into something we all take for granted in language: word structure . In NLP, we call this study morphology . This post is all about how computers can break down and understand words—just like humans do—with the help of four key tools: Morphemes Stemming Lemmatization Lexicon 1. Morphemes – The Building Blocks 🧱 A morpheme is the smallest unit in a word that carries meaning. These include roots like “run”, prefixes like “un-”, and suffixes like “-ing”. Let’s look at an example: "unbelievably" → "un" (negation) + "believe" (root) + "able" (capable of) + "ly" (manner) Understanding morphe...

Understanding Morphology in NLP: The Key to Word-Level Language Intelligence Understanding Morphology in NLP A Deep Dive into Telugu vs English Morphological Patterns TL;DR This post introduces Morphology in NLP using real language examples (Telugu vs English). Learn how word forms change and how machines understand them. Welcome to the fascinating world of morphology in Natural Language Processing! Today, we're diving deep into how different languages structure their words, and why this matters immensely for building intelligent language systems. What is Morphology? Morphology is the study of word structure - how words are formed and how they change their forms to express different meanings. In NLP, understanding morphology is crucial because it helps machines recognize relationships between different word forms and extract meaningful information from text. Think of morpheme...