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Text Analytics for Business (BUSA90543)
Graduate courseworkPoints: 12.5On Campus (Parkville)
Overview
Availability | May |
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Fees | Look up fees |
This component helps students develop an understanding of the key algorithms used in natural language processing and text retrieval, for use in a diverse range of applications including search engines, cross-language information retrieval, machine translation, text mining, question answering, summarisation, and grammar correction. Topics to be covered include text normalisation, sentence boundary detection, part-of-speech tagging, n-gram language modelling, sentiment analysis, web mining and analysis, network analysis (including social network analysis), and text classification.
Intended learning outcomes
On completion of this subject, students should be able to:
- Develop and evaluate computational models of language.
- Articulate issues relevant to the efficient implementation of language processing systems and text retrieval systems.
- Apply natural language processing and information retrieval methodologies to textual data.
Last updated: 5 June 2024