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  6. CS4025: NATURAL LANGUAGE PROCESSING

CS4025: NATURAL LANGUAGE PROCESSING (2016-2017)

Last modified: 28 Jun 2018 10:27


Course Overview

Natural Language Processing (NLP) is an influential topic that relates to Artificial Intelligence, Linguistics and Human Computer Interaction. NLP engineers are in high demand at companies such as Google, Facebook, Twitter, Yahoo and Microsoft that require sophisticated analysis of text on the internet. This course covers a range of theoretical and applied topics related to how computers interpret human language, and also how computers can generate human language; for example, to summarise data. Topics include grammar formalisms and algorithms for parsing sentences, natural language semantics, text analytics using sentiment analysis, machine translation, grammar checking and natural language generation from data.

Course Details

Study Type Undergraduate Level 4
Term First Term Credit Points 15 credits (7.5 ECTS credits)
Campus None. Sustained Study No
Co-ordinators
  • Dr Advaith Siddharthan
  • Dr Chenghua Lin

Qualification Prerequisites

None.

What courses & programmes must have been taken before this course?

  • One of CS3015 Software Engineering: Principles and Practice (Passed) or CS3024 Software Engineering and Professional Issues (Passed) or CS3528 Software Engineering and Professional Practice (Passed)
  • One of CS3015 Software Engineering: Principles and Practice (Passed) or CS3024 Software Engineering and Professional Issues (Passed) or CS3028 Principles of Software Engineering (Passed)
  • Any Undergraduate Programme (Studied)

What other courses must be taken with this course?

None.

What courses cannot be taken with this course?

None.

Are there a limited number of places available?

No

Course Description

  • Formal linguistic models of English: word categories, sentence constituents, phrase-structure grammar rules, features. Modelling syntactic phenemena.
  • Parsing: shift-reduce parsers, chart parsers, handling ambiguity, definate clause grammars.
  • Semantics and pragmatics: meaning representations, reference, speech acts.
  • Generation: Content determination, sentence planning, and realisation.
  • Applications: grammar checking, machine translation, database interfaces, report generation, dictation.
  • Speech: Hidden Markov Models, statistical langauge models, speech synthesis.

Further Information & Notes

(i) Assistive technologies may be required for any student who is unable to use a standard keyboard/mouse/computer monitor. Any students wishing to discuss this further should contact the School Disability Co-ordinator.

Contact Teaching Time

Information on contact teaching time is available from the course guide.

Teaching Breakdown

More Information about Week Numbers


Details, including assessments, may be subject to change until 30 August 2024 for 1st term courses and 20 December 2024 for 2nd term courses.

Summative Assessments

1st Attempt: 1 two-hour written examination (75%); continuous assessment (25%).

Resit: 1 two-hour written examination (75%); continuous assessment mark carried forwards (25%).

Only the marks obtained at first sitting can be used for Honours classification.

Formative Assessment

During lectures, the Personal Response System and/or other ways of student interaction will be used for formative assessment. Additionally, practical sessions will provide students with practice opportunities and formative assessment.

Feedback

Formative feedback for in-course assessments will be provided in written form. Additionally, formative feedback on performance will be provided informally during practical sessions.

Course Learning Outcomes

None.
 

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