NATURAL LANGUAGE PROCESSING
Credit points: 15
Natural Language Processing (NLP) is broadly concerned with the interactions between computers and natural (i.e., human) languages; more particularly, it is concerned with the question of how to program computers to process and analyse large amounts of natural language data. Following a review of the essential mathematical and linguistic concepts underlying natural language processing, you will develop skills in important natural language processing sub-tasks including accessing corpora, tokenisation, morphological analysis, word sense disambiguation, part-of speech tagging, and analysing sentence structure. You will then apply these skills in the context of applications such as text categorisation, text clustering, text recommendation, and information retrieval. Where appropriate, both lexical (i.e. dictionary-based) and machine learning approaches will be used.
SchoolEngineering and Mathematical Sciences
Subject Co-ordinatorAndrew Skabar
Available to Study Abroad/Exchange StudentsYes
Subject year levelYear Level 5 - Masters
Available as ElectiveNo
PrerequisitesCSE5AIF OR CSE2AIF
Quota Management StrategyN/A
Quota-conditions or rulesN/A
Minimum credit point requirementN/A
Foundations of Statistical Natural Language Processing
AuthorChristopher D. Manning and Hinrich Schutze.
PublisherThe MIT Press
Speech and Language Processing
AuthorDaniel Jurafsky and James H. Martin.
Natural Language Processing with Python
AuthorSteven Bird, Ewan Klein & Edward Loper
Self sourced or Uni sourcedN/A
Entire subject or partial subjectN/A
Total hours/days requiredN/A
Location of WBL activity (region)N/A
WBL addtional requirementsN/A
Graduate capabilities & intended learning outcomes
Intended Learning Outcomes
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Melbourne (Bundoora), 2021, Summer, Day
Maximum enrolment sizeN/A
Subject Instance Co-ordinatorAndrew Skabar
Computer LaboratoryWeek: 46 - 0
One 2.00 h computer laboratory per week on weekdays during the day from week 46 to week 0 and delivered via face-to-face.
LectureWeek: 46 - 0
One 2.00 h lecture per week on weekdays during the day from week 46 to week 0 and delivered via face-to-face.
|Assignment 1 (equivalent to 1500 words) Students write Python code to perform tokenisation, morphological analysis, word sense disambiguation and part-of-speech tagging.||N/A||N/A||No||20||SILO1, SILO2|
|Assignment 2 (equivalent to 2,000 words) Students apply their skills to tasks such as text categorisation, text clustering, text recommendation and information retrieval.||N/A||N/A||No||30||SILO2, SILO3, SILO4|
|Two-hour examination (equivalent to 2,000 words)||N/A||N/A||No||50||SILO1, SILO2, SILO3, SILO4|