- Code COMP4650
- Unit Value 6 units
- Offered by Research School of Computer Science
- ANU College ANU College of Engineering and Computer Science
- Course subject Computer Science
- Academic career UGRD
- Dr Lexing Xie
- Mode of delivery In Person
- Co-taught Course
Second Semester 2018
See Future Offerings
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:Upon successful completion of the course, the student will have an understanding of the role documents play in business and community, and the various digital resources available for document analysis. Moreover, the student will have the background theory and practical knowledge necessary to plan and execute a basic document analysis project. The student will be able 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.
Assignments (40%); Written final exam (60%).
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WorkloadTwenty four one-hour lectures and ten two-hour 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.
Tuition fees are for the academic year indicated at the top of the page.
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- Student Contribution Band:
- Unit value:
- 6 units
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Offerings, Dates and Class Summary Links
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Class summaries, if available, can be accessed by clicking on the View link for the relevant class number.
|Class number||Class start date||Last day to enrol||Census date||Class end date||Mode Of Delivery||Class Summary|
|9397||23 Jul 2018||30 Jul 2018||31 Aug 2018||26 Oct 2018||In Person||N/A|