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Department of Computer Science and Technology



Course pages 2021–22

Natural Language Processing

Principal lecturer: Prof Simone Teufel
Additional lecturers: Dr Andrew Caines, Dr Guy Emerson
Taken by: MPhil ACS, Part III
Code: L90
Term: Michaelmas
Hours: 18 (12 lectures and 3 x 2 hour practical sessions)
Format: In-person lectures
Class limit: max. 10 students
Prerequisites: No prerequisites beyond those topics covered in an undergraduate CS degree. This course is a prerequisite for L95: Introduction to Natural Language Syntax and Parsing if you haven't already done a NLP course
This course is a prerequisite for: Introduction to Natural Language Syntax and Parsing


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, some 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. Some limitations of context-free grammars.
  • Dependency structures. English as an outlier. Universal dependencies. Introduction to dependency parsing.
  • Compositional semantics. Logical representations. Compositional semantics and lambda calculus. Inference and robust entailment. Negation.
  • Lexical semantics. Semantic relations, WordNet, word senses.
  • Distributional semantics. Representing lexical meaning with distributions. Similarity metrics.
  • Distributional semantics and deep learning. Embeddings. Grounding. Multimodal systems and visual question answering.
  • Discourse processing. Anaphora resolution, summarization.
  • Language generation and regeneration. Generation and regeneration. Components of a generation system. Generation of referring expressions.
  • Recent NLP research. Some recent NLP research.
  • Practical on information extraction. Students will build named entity recognition systems 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 some programming will be required for this.


By the end of the course 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.

Assessment - Part II Students

  • Assignment 1 - 10% of marks
  • Assignment 2 -  10% of marks
  • Assignment 3 -  80% of marks

Recommended reading

* Jurafsky, D. and Martin, J. (2008) Speech and language processing. Prentice Hall.


Undertake 2 ticked exercises as part of practical sessions on information extraction.

Write a 4,000-word report including results from an extended information extraction experiment.

Practical work

Build and evaluate a NLP system.

Assessment - Part III and MPhil Students

Assessment will be based on the practicals:

  • First practical exercise (10%, ticked)
  • Second practical exercise (10%, ticked)
  • Final report (80%, 4,000 words, excluding references)

Further Information

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

Due to COVID-19, the method of teaching for this module will be adjusted to cater for physical distancing and students who are working remotely. We will confirm precisely how the module will be taught closer to the start of term.

  • Current Cambridge undergraduate students who are continuing onto Part III or the MPhil in Advanced Computer Science may only take this module if they did NOT take it as a Unit of Assessment in Part II.
  • The class limit is 10 MPhil / Part III students with the practical assessed by the Departent of Computer Science and Technology.
  • Students from other departments may attend the lectures for this module if space allows. However students wanting to take it for credit will need to make arrangements for assessment within their own department.

This module is shared with Part II of the Computer Science Tripos. Assessment will be adjusted for the two groups of students to be at an appropriate level for whichever course the student is enrolled on. Further information about assessment and practicals will follow at the first lecture.