References for further study
NLP is a vast field of study with a broad scope, and it can be quite challenging for beginners to know where to start or which concepts are the most important to understand. In this section, we provide a few references for further study.
For beginners interested in learning more about NLP, apart from this tutorial there are many online resources available. Some good starting points include:
The NLTK book provides an introduction to NLP with Python.
Another great resource for beginners interested in NLP is the book “Speech and Language Processing” by Jurafsky and Martin. This textbook provides a comprehensive introduction to NLP, covering topics such as language modelling, part-of-speech tagging, syntax and parsing, and machine translation. The book is available online for free here. In addition to the book itself, the website provides supplementary materials, such as lecture slides and programming exercises, that can help readers deepen their understanding of the material.
The article A beginner’s guide to natural language processing on the IBM Developer website provides a high-level overview of NLP and its importance in machine learning, covering topics such as text preprocessing, feature extraction, and model selection.
The article of Stanford Encyclopedia of Philosophy on the broader area of computational linguistics.
The Coursera NLP Specialization course covers a wide range of topics in NLP with video lectures and hands-on assignments.
The Stanford NLP group provides a wealth of resources, including research papers, datasets, and open-source software.