The University of Sheffield
Department of Computer Science

COM4513 Natural Language Processing

Summary This module provides an introduction to the field of computer processing of written natural language, known as Natural Language Processing (NLP). We will cover standard theories, models and algorithms, discuss competing solutions to problems, describe example systems and applications, and highlight areas of open research. Students should be aware that there are limited places available on this course.
Session Spring 2022/23
Credits 15

Assignment and formal exam

Lecturer(s) Dr Chenghua Lin & Dr Nafise Sadat Moosavi
  • to give students a well-rounded feel for the problems and approaches of Statistical Natural Language Processing (NLP)
  • to give students an understanding of the potential areas of application of the techniques developed in Statistical NLP
Objectives By the end of this course the students should:
  • be able to describe and discuss the subareas of NLP
  • be able to implement NLP algorithms and techniques;
  • be able to describe and discuss the potential and limitations of NLP techniques for applications such as machine translation, question answering, information retrieval and information extraction

Lectures will provide an overview of the field of NLP and its sub-areas, and will introduce and explain its key techniques, including their applicability and limitations. In lab classes, students will practice implementing the NLP techniques taught in class, testing their code in application to real language data. Topics covered will include:

  • N-gram Language Modelling
  • Word Classes and Part-of-Speech Tagging
  • Lexical Semantics, Word Sense Disambiguation and Lexical Similarity
  • Syntactic and semantic parsing
  • Information extraction
  • Neural network architectures for NLP
Restrictions This module is only open to students who have taken Text Processing (COM3110/COM4115/COM6115) and Machine Learning and Adaptive Intelligence (COM4509/6509).
Teaching Method There will be 2 formal lectures and 1 lab session per week.
Feedback Problem sheets will be set during labs sessions and then will discussed in labs and/or lectures.
Verbal interaction during lectures.
Recommended Reading
  • Daniel Jurafsky and James Martin. 2008. "Speech and Language Processing" Prentice Hall. (A draft of the 3d edition can be found here:
  • Christopher D. Manning and Hinrich Schütze. 1999. "Foundations of Statistical Natural Language Processing", MIT Press.
  • Yoav Goldberg. 2017. Neural Network Methods in Natural Language Processing (Synthesis Lectures on Human Language Technologies), Morgan & Claypool Publishers.