- Code COMP6990
- Unit Value 6 units
- Offered by School of Computing
- ANU College ANU College of Engineering Computing & Cybernetics
- Course subject Computer Science
- Academic career PGRD
- Mode of delivery In Person
Processing of semi-structured documents such as internet pages, RSS feeds and their accompanying news items, and PDF brochures is considered from the perspective of interpreting the content. This course considers the \document" and its various genres as a fundamental object for business, government and community. For this, the course covers four broad areas: (A) information retrieval, (B) natural language processing, (C) machine learning for documents, and (D) relevant tools for the Web. Basic tasks here are covered including content collection and extraction, formal and informal natural language processing, information extraction, information retrieval, classification and analysis. Fundamental probabilistic techniques for performing these tasks, and some common software systems will be covered, though no area will be covered in any depth.
Upon successful completion, students will have the knowledge and skills to:
- Differentiate between the basic probabilistic theories of language and document structure, information retrieval, and classification, clustering and document feature engineering.
- Identify the basic algorithms and software available for probabilistic theories of language and be proficient at using common libraries for natural language processing to perform basic analysis tasks.
- Index a document collection for use in an information retrieval system. Demonstrate advanced knowledge of basic theories and algorithms to determine large scale named-entity matching and standardization of names within a collection.
- Perform automated classification using probabilistic theories.
You should be enrolled in the Master of Applied Data Analytics to undertake this blended intensive course.
Note: Non-MADAN students wanting to enrol are required to seek approval from their Program Convener.
- Assignments (40) [LO 1,2,3,4]
- Written Final Exam (60) [LO 1,2,3,4]
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Thirty one-hour lectures and six two hour tutorial/laboratory sessions
Requisite and Incompatibility
The following reference books will be used.
- Introduction to Information Retrieval, C.D. Manning, P. Raghavan and H. Scutze, Cambridge University Press, 2008.
- Foundations of Statistical Natural Language Processing, C.D. Manning and H. Scutze, MIT Press, 1999
Programming ability in C, C++, Java or Python, and basic mathematical and statistical knowledge, at an undergraduate-level
Tuition fees are for the academic year indicated at the top of the page.
Commonwealth Support (CSP) Students
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