Quick tech ticks and comparisons
Topics in the Natural Language Processing category.
A beginner-friendly introduction to Natural Language Processing. Learn what NLP is, where it's used across industries, the stages of the NLP pipeline, and the different approaches to building NLP systems—using simple stories and real-world examples.
A beginner-friendly guide to Lexical Processing in NLP. Learn how computers break down text through tokenization, text normalization, stopword removal, stemming, lemmatization, and spell correction—with simple explanations and Python examples.
Learn how computers identify word types (nouns, verbs, adjectives) and group them into meaningful phrases. A beginner-friendly guide to Part-of-Speech tagging and shallow parsing with Python examples.
Learn how computers understand sentence structure through grammar rules, constituency parsing, and dependency parsing. A beginner-friendly guide to analyzing how words relate to each other.
Learn how to visualize sentence structure with parse trees and ensure grammatical correctness through agreement checking. The final part of our syntactic processing series.
A beginner-friendly guide to how computers understand word meaning. Covers lexical semantics, word sense disambiguation, co-occurrence models, and how to measure word similarity using statistics.
Learn how computers identify who is doing what to whom in a sentence. Covers Semantic Role Labelling, Named Entity Recognition, IOB tagging, Conditional Random Fields, and Coreference Resolution—with simple explanations and Python examples.
Learn how computers turn raw text into numbers, and how to discover hidden topics in large collections of documents—without any labelled data. Covers Bag-of-Words, TF-IDF, Word Embeddings, and Topic Modelling with NMF.
A beginner-friendly guide to classical text generation. Learn how computers generate language using N-gram models, Markov chains, probability, smoothing techniques, and how to evaluate quality with perplexity—before the era of neural networks.
Learn how classical NLP systems generate sentences, automatically summarise documents, and translate between languages—before neural networks. Covers controlled sentence generation, extractive summarisation, and rule-based and statistical machine translation.
A quick-reference cheat sheet covering every important NLP term and concept from the full tutorial series. Organised by topic—from tokenisation to machine translation—with plain-English definitions and one-line memory aids.
A complete reference guide to every Python library used across the NLP tutorial series. Learn what each library does, how to install it, and which functions to use for each NLP task—with ready-to-run code examples.