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HE Unit Template
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Natural Language Processing
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AIWSU7022 - Natural Language Processing
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20250200
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Course
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Unit
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1.1
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2026-09-10
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2026-09-10
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Information Technology
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Institute of Innovation, Science & Sustainability
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Information Technology
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Postgraduate - Coursework
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15
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.125
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029999
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Information Technology, n.e.c.
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029999 - Information Technology, n.e.c.
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<a href="http://www.federation.edu.au/locate/unit/aiwsu7022">View unit fees</a>
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Graded (e.g. HD, D, C, etc)
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9
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Intermediate
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<p>Natural Language Processing involves machine reading comprehension and the technologies using it are becoming increasingly widespread. This subject provides a foundation in using the Natural Language Toolkit, which is a leading platform for building Python programs working with 'human language' data, as well as an introduction to Python for Natural Language Processing. Students will use algorithms and explore accessing text corpora and processing raw text; categorising words and classifying text; understanding information from text and analysing sentence structures; and understanding semantic meanings of sentences. Students also gain real-world hands-on experience with Natural Language Processing through the practical tasks and assignments.</p>
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<p>• Installing and introducing the Natural Language Toolkit (NLKT):</p>
<p>-Introducing the Python programming language with emphasise for Natural Language Processing (NLP);</p>
<p>-Using Python string datatypes for the processing of words and texts.</p>
<p>• Accessing text corpora (i.e., etext repositories) and the processing of raw text:</p>
<p>-Introduces the Gutenberg, Reuters, Inaugural Address and Annotated text corpus as well as Web and Chat text;</p>
<p>-Looking at text corpus structure.</p>
<p>• Categorizing words and classifying text:</p>
<p>-Tagging corpora;</p>
<p>-Mapping words to properties through Python Dictionaries;</p>
<p>-Transformation based tagging;</p>
<p>-Determining the category of a word;</p>
<p>-Supervised, decision trees, naive bayes and ent</p>
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<p>Where supplementary assessment is available a student must have failed overall in the Unit but gained a final mark of 45 per cent or above, has completed all major assessment tasks (including all sub-components where a task has multiple parts) as specified in the Unit Description and is not eligible for any other form of supplementary assessment.</p>
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Semester
0200
Year
Year
2027
Current Year
2026
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AIWSU7022 - Natural Language Processing
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AIWSU7022 - Natural Language Processing
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Semester
Semester 1
Semester 2
Summer Semester
Year
2027
2026
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Overview
Key information
Requisites
Learning outcomes
Assessment
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Overview
Unit description
<p>Natural Language Processing involves machine reading comprehension and the technologies using it are becoming increasingly widespread. This subject provides a foundation in using the Natural Language Toolkit, which is a leading platform for building Python programs working with 'human language' data, as well as an introduction to Python for Natural Language Processing. Students will use algorithms and explore accessing text corpora and processing raw text; categorising words and classifying text; understanding information from text and analysing sentence structures; and understanding semantic meanings of sentences. Students also gain real-world hands-on experience with Natural Language Processing through the practical tasks and assignments.</p>
Unit content
<p>• Installing and introducing the Natural Language Toolkit (NLKT):</p> <p>-Introducing the Python programming language with emphasise for Natural Language Processing (NLP);</p> <p>-Using Python string datatypes for the processing of words and texts.</p> <p>• Accessing text corpora (i.e., etext repositories) and the processing of raw text:</p> <p>-Introduces the Gutenberg, Reuters, Inaugural Address and Annotated text corpus as well as Web and Chat text;</p> <p>-Looking at text corpus structure.</p> <p>• Categorizing words and classifying text:</p> <p>-Tagging corpora;</p> <p>-Mapping words to properties through Python Dictionaries;</p> <p>-Transformation based tagging;</p> <p>-Determining the category of a word;</p> <p>-Supervised, decision trees, naive bayes and ent</p> <p><br></p>
Key information
Institute / school:
Institute of Innovation, Science & Sustainability
Discipline:
Information Technology
Study level:
Postgraduate - Coursework
Credit points:
15
EFTSL:
0.125
Field of education:
029999 - Information Technology, n.e.c.
Grade scheme:
Graded (e.g. HD, D, C, etc)
Placement component:
No
Fee information:
<a href="http://www.federation.edu.au/locate/unit/aiwsu7022">View unit fees</a>
Requisites
Prerequisite
None
Corequisite
None
Exclusion
None
Learning outcomes
Learning outcomes
<p>On successful completion of this unit, students are expected to gain the following [K]nowledge, [S]kills and [A]pplication of knowledge & skills</p>
Learning Outcomes
Ref
Outcome
K1
<p>Determine the role of classification of words and text in Natural Language Processing algorithms.</p>
K2
<p>Clarify the function of Natural Language Processing algorithms in understanding information from text and the semantic meanings of sentences.</p>
S1
<p>Apply the Natural Language Toolkit to real world Natural Language Processing tasks.</p>
S2
<p>Use text corpora for the engineering and evaluation of Natural Language Processing systems.</p>
A1
<p>Analyse artificial intelligence techniques employed in Natural Language Processing algorithms.</p>
A2
<p>Manage linguistic data.</p>
Assessment
Learning Tasks and Assessment
Ref
Learning task
Assessment type
Weighting
Learning outcomes assessed
1
<p>1hour quiz</p>
<p>Quiz</p>
<p>30%</p>
K1, K2, A1, A2
2
<p>2- hour practical</p>
<p>Practical</p>
<p>30%</p>
S1, S2, A2
3
<p>1500-word report</p>
<p>report</p>
<p>40%</p>
S1, S2, A2
Supplementary assessment available:
Yes
Supplementary assessment information:
<p>Where supplementary assessment is available a student must have failed overall in the Unit but gained a final mark of 45 per cent or above, has completed all major assessment tasks (including all sub-components where a task has multiple parts) as specified in the Unit Description and is not eligible for any other form of supplementary assessment.</p>
Key information
Institute / school:
Institute of Innovation, Science & Sustainability
Discipline:
Information Technology
Study level:
Postgraduate - Coursework
Credit points:
15
EFTSL:
0.125
Field of education:
029999 - Information Technology, n.e.c.
Grade scheme:
Graded (e.g. HD, D, C, etc)
Placement component:
No
Fee information:
<a href="http://www.federation.edu.au/locate/unit/aiwsu7022">View unit fees</a>
Additional information
Add Info Static Text
Internal reference: AIWSU7022 - U1.1