nlp
Progress from zero to frontier with a guided depth ladder.
What is NLP? (Natural Language Processing)
NLP explained simply: how computers work with human language.
NLP from Rules to Transformers — What Changed and What Still Matters
A concise history of NLP evolution and the enduring principles teams still need for modern language systems.
Coreference Resolution: Teaching AI to Track Who's Who in Text
When text says 'she,' 'the company,' or 'it,' something needs to figure out what those words refer to. Coreference resolution is the NLP task of linking mentions to entities — and it's harder than it sounds.
NLP Evaluation Playbook in 2026: Beyond Accuracy
A practical NLP evaluation framework for modern systems spanning classification, extraction, search, QA, and generative behavior.
Keyword Extraction and Topic Modeling: Making Sense of Large Text Collections
You have 100,000 customer reviews. What are people talking about? Keyword extraction and topic modeling surface the themes, trends, and patterns hidden in large text collections.
Evaluating Language Models: Metrics, Benchmarks, and What Actually Matters
BLEU, ROUGE, perplexity, MMLU — the metrics used to evaluate language models are often misunderstood. This guide explains what each measures, when to use it, and why leaderboard scores don't tell the whole story.
NLP for Legal Documents: Contract Analysis with AI
AI-powered contract analysis is one of NLP's most mature enterprise applications. Here's how it works, what it can reliably do, and where human lawyers remain essential.
Building Question Answering Systems: From Extractive to Generative
The engineering of question answering systems — from traditional extractive QA to modern RAG-based approaches. What each approach is good for, where they fail, and how to choose.
Sentiment Analysis: How AI Understands Tone and Opinion
Sentiment analysis — detecting positive, negative, or nuanced emotion in text — is one of the most widely deployed NLP tasks. Here's how it works, what it can and can't do, and how to use it.
Sentiment Analysis in Production: Beyond Positive and Negative
How to build sentiment analysis that actually works in production — from choosing your approach to handling the messy reality of user-generated text.
Text Summarization: From Extractive to Abstractive to LLM-Powered
Summarization has evolved from sentence extraction to sophisticated LLM-powered condensation. This guide covers techniques, trade-offs, and practical implementation.
Evaluating Text Generation: Metrics, Methods, and What Actually Works
How do you measure whether generated text is good? BLEU and ROUGE have known flaws. LLM-as-judge is promising but imperfect. This guide covers the full evaluation landscape for text generation.
Text Preprocessing in 2026: What Still Matters and What Doesn't
A modern guide to text preprocessing — what's still necessary in the age of LLMs, what's been made obsolete, and the preprocessing steps that actually improve your NLP pipeline.
Modern NLP: How Language Understanding Works in 2026
A technical survey of modern NLP — from foundational tasks and pre-transformer approaches to the transformer revolution, current SOTA, and where the field is heading in 2026.
Information Extraction in NLP
Turning messy text into structured data is one of NLP's most valuable jobs. Here's how information extraction works, what systems need to capture, and why evaluation is harder than it looks.
NLP for Multilingual Applications
A technical guide to building multilingual NLP systems—cross-lingual models, machine translation, multilingual embeddings, localization challenges, and practical strategies for serving users in multiple languages.
Named Entity Recognition: From Rules to Neural Networks
Named entity recognition is one of NLP's fundamental tasks. This guide covers how NER evolved, how modern neural approaches work, and how to use it in practice.
Relation Extraction: Building Knowledge Graphs from Unstructured Text
How to extract structured relationships from unstructured text — from rule-based systems to transformer models — and build knowledge graphs that power search, QA, and reasoning systems.
Text Classification with NLP: From Rules to Transformers
Text classification is one of NLP's most practical tasks. Here's how modern approaches work, how to choose the right method, and how to build reliable classifiers.