Understanding NLP Token Classification: NER, POS Tagging & Chunking Explained Simply
Continue reading on Medium »
nlp
Artificial Intelligence, data-science, Machine Learning, nlp, python
Mastering Text Intelligence: A Beginner’s Guide to NLP Token Classification
In the modern era of Artificial Intelligence, we often marvel at how ChatGPT can write poetry or how Google seems to know exactly what we…Continue reading on Medium »
Artificial Intelligence, data-analysis, data-science, Machine Learning, nlp
How Does AI Know What Kind of News You Are Reading? — Part 4
Tuning the Bernoulli Naïve Bayes Model for News Classification, all from First Principles.Continue reading on Towards AI »
Agents, graphdb, knowledge-graph, Machine Learning, nlp
From Word Clouds to Knowledge Graphs: A Practical NLP Path for Developers
David Balkcom, Principal EngineerWhen people first start exploring text analysis, they often land on a familiar visual: the word cloud. It is fast, intuitive, and useful for a rough first pass. But if your goal is to extract meaning, model relationship…
ai-ethics, hate-speech, llm, Machine Learning, nlp
Hate Speech Detection Still Cooks (Even in 2026)
The failure case you didn’t see comingIn late 2025, a major social platform quietly rolled back parts of its LLM-based moderation pipeline after internal audits revealed a systematic pattern: posts in African American Vernacular English (AAVE) were fla…
data-engineering, knowledge-graph, Machine Learning, nlp, python
From Text to Knowledge Graph in One Command: Building a Modular LLM-Backed Framework
How abstraction, registries, and a clean CLI turn chaotic text into structured, queryable knowledge — without locking you into a single…Continue reading on Towards AI »
Artificial Intelligence, clustering, llm, Machine Learning, nlp
You Don’t Need RAG. You Need Semantic Compression.
How I solved a chunk selection problem that the current state of the art overlooks and why K-means is the unexpected answerSource: Image by the authorThere’s a class of LLM problems that has no name, no framework, and almost no literature.The problem: …
data-analysis, gpt, Machine Learning, nlp, transformers
From Logistic Regression to GPT-2: Building a Complete Spam Detection & Sentiment Analysis Pipeline
Benchmarking eight models across three paradigms revealing why accuracy is a deceptive metric for imbalanced text classificationTable of ContentsThe Dataset & Class ImbalanceWhat Spam Looks Like in WordsPhase 1 : The Eight-Model BenchmarkHow All Ei…
cosine similarity, embeddings, Machine Learning, nlp, RAG, semantic-search, sentence transformers, tf-idf, tutorial, Vector Databases
TF-IDF vs. Embeddings: From Keywords to Semantic Search
Table of Contents TF-IDF vs. Embeddings: From Keywords to Semantic Search Series Preamble: From Text to RAG What You’ll Build Across the Series Project Structure Why Start with Embeddings The Problem with Keyword Search When “Different Words” Mean the Same…
The post TF-IDF vs. Embeddings: From Keywords to Semantic Search appeared first on PyImageSearch.
Interpretability, llm, nlp
Do text embeddings perfectly encode text?
‘Vec2text’ can serve as a solution for accurately reverting embeddings back into text, thus highlighting the urgent need for revisiting security protocols around embedded data.