- Code COMP4650
- Unit Value 6 units
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:
- Understand the basic requirements digital libraries and business processes have w.r.t. documents, in different formats such as XML, RDF, HTML, CSS, JS and RSS.
- Understand the genres of documents available from the internet such as RSS feeds, social networks, blogs, wikis, archives, etc., and the role they play in the internet ecosystem. Understand the linguistic and semantic resources available from the internet and the so-called ``web of data'', such as dictionaries, repositories and ontologies
- Understand basic probabilistic theories of language and document structure, and the basic algorithms and software available for them, and be able to use some common libraries for natural language processing to perform basic analysis tasks.
- Understand basic probabilistic theories of information retrieval, and be able to index a document collection for use in an information retrieval system. Understanding basic theories and algorithms for large scale named-entity matching and standardization of names within a collection.
- Understand basic probabilistic theories of classification, clustering, and document feature ``engineering'', and be able to perform automated classification.
Assignments (40%); Written final exam (60%).
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Twenty 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.
If you are a domestic graduate coursework or international student you will be required to pay tuition fees. Tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found at Fees.
- Student Contribution Band:
- Unit value:
- 6 units
If you are an undergraduate student and have been offered a Commonwealth supported place, your fees are set by the Australian Government for each course. At ANU 1 EFTSL is 48 units (normally 8 x 6-unit courses). You can find your student contribution amount for each course at Fees. Where there is a unit range displayed for this course, not all unit options below may be available.
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|
|2355||20 Jul 2015||07 Aug 2015||31 Aug 2015||30 Oct 2015||In Person||N/A|