Computer Laboratory

Course pages 2014–15

Overview of Natural Language Processing

Principal lecturers: Prof Ann Copestake, Dr Simone Teufel
Taken by: MPhil ACS, Part III
Code: L90
Hours: 16 (12 lectures and 2 x 2 hour practical sessions)
Prerequisites: Courses such as CST Part II Regular Languages and Finite Automata, Probability, Logic and Proof, and Artificial Intelligence


This course introduces the fundamental techniques of natural language processing. It aims to explain the potential and the main limitations of these techniques. Some current research issues are introduced and some current and potential applications discussed and evaluated. Students will also be introduced to practical experimentation in natural language processing.


  • Introduction. Brief history of NLP research, current applications, components of NLP systems.
  • Finite-state techniques. Inflectional and derivational morphology, finite-state automata in NLP, finite-state transducers.
  • Prediction and part-of-speech tagging. Corpora, simple N-grams, word prediction, stochastic tagging, evaluating system performance.
  • Context-free grammars and parsing. Generative grammar, context-free grammars, parsing with context-free grammars, weights and probabilities. Limitations of context-free grammars.
  • Constraint-based grammars. Constraint-based grammar, unification.
  • Compositional semantics. Simple compositional semantics in constraint-based grammar. Compositional semantics with lambda calculus. Inference and robust entailment.
  • Lexical semantics. Semantic relations, WordNet, word senses, word sense disambiguation.
  • Distributional semantics Representing lexical meaning with distributions. Similarity metrics. Clustering.
  • Discourse and dialogue. Anaphora resolution, discourse relations.
  • Language generation Realization as inverse of parsing. N-grams and fluency. Text simplification.
  • Computational psycholinguistics: modelling human language use
  • Applications. Examples of practical applications of NLP techniques.
  • Practical on sentiment analysis. Students will build a sentiment analysis system which will be trained and evaluated on supplied data. The system will be built from existing components, but students will be expected to compare approaches and to implement their own feature extraction to support this.


On completion of this module, students should:

  • be able to discuss the current and likely future performance of several NLP applications;
  • be able to describe briefly a fundamental technique for processing language for several subtasks, such as morphological processing, parsing, word sense disambiguation etc.;
  • understand how these techniques draw on and relate to other areas of computer science;
  • understand the basic principles of designing and running an NLP experiment.


Write a 4,000-word report including results from a sentiment analysis experiment based on the practical component of the course.

Practical work

Build and evaluate a sentiment analysis system.


Assessment will be based on the 4,000-word practical report.

Recommended reading

Jurafsky, D. and Martin, J. (2008). Speech and language processing. Prentice Hall (specific chapter references will be provided in the lecture notes).

Although the lectures don't assume any exposure to linguistics, the course will be easier to follow if students have some understanding of basic linguistic concepts. The following may be useful for this: The Internet Grammar of English