Introduction-NLP

Why we included this project

These are detailed study notes from a reader working through the book 'Introduction to Natural Language Processing' by the creator of HanLP. The chapters cover the main algorithms behind Chinese NLP: dictionary-based segmentation, hidden Markov models, conditional random fields, part-of-speech tagging, named entity recognition, information extraction, text clustering and classification, and dependency parsing. The notes translate the math into plain language and include engineering details, so they work better as a guided tutorial than a reference text. If you are new to NLP, this is a practical bridge between formulas and code. If you already build models, it is a fast way to refresh the foundations that sit under modern language models. This is study material, not a library, so use it to build your own understanding rather than to run in production.