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  3. Natural Language Processing

Natural Language Processing (COMP90042)

Graduate courseworkPoints: 12.5On Campus (Parkville)

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Overview

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Semester 1
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AIMS

Much of the world's knowledge is stored in the form of text, and accordingly, understanding and harnessing knowledge from text are key challenges. In this subject, students will learn computational methods for working with text, in the form of natural language understanding, and language generation. Students will develop an understanding of the main algorithms used in natural language processing, for use in a diverse range of applications including machine translation, text mining, sentiment analysis, and question answering. The programming language used is Python.

INDICATIVE CONTENT

Topics covered may include:

  • Text classification and unsupervised topic discovery
  • Vector space models for natural language semantics
  • Structured prediction for tagging
  • Syntax models for parsing of sentences and documents
  • N-gram language modelling
  • Automatic translation, and multilingual methods
  • Relation extraction and coreference resolution

Intended learning outcomes

On completion of this subject the student is expected to:

  • Identify basic challenges associated with the computational modelling of natural language
  • Understand and articulate the mathematical and/or algorithmic basis of common techniques used in natural language processing
  • Implement relevant techniques and/or interface with existing libraries
  • Carry out end-to-end research experiments, including evaluation with text corpora as well as presentation and interpretation of results
  • Critically analyse and assess text-processing systems and communicate criticisms constructively

Generic skills

On completing this subject, students should have the following skills:

  • Formulate and implement algorithmic solutions to computational problems, with reference to the research literature
  • Apply a systems approach to complex problems, and design for operational efficiency
  • Design, implement and test programs for small and medium size problems in the Python programming language

 

Last updated: 31 January 2024