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.
Learning Outcomes
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.
Indicative Assessment
Assignments (40%); Written final exam (60%).
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Workload
Twenty four one-hour lectures and ten two-hour laboratory sessions.Requisite and Incompatibility
Prescribed Texts
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.
Majors
Fees
Tuition fees are for the academic year indicated at the top of the page.
Commonwealth Support (CSP) Students
If you 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). More information about your student contribution amount for each course at Fees.
- Student Contribution Band:
- 2
- Unit value:
- 6 units
If you are a domestic graduate coursework student with a Domestic Tuition Fee (DTF) place or international student you will be required to pay course tuition fees (see below). Course tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found at Fees.
Where there is a unit range displayed for this course, not all unit options below may be available.
| Units | EFTSL |
|---|---|
| 6.00 | 0.12500 |
Course fees
- Domestic fee paying students
| Year | Fee |
|---|---|
| 2022 | $4740 |
- International fee paying students
| Year | Fee |
|---|---|
| 2022 | $6000 |
Offerings, Dates and Class Summary Links
ANU utilises MyTimetable to enable students to view the timetable for their enrolled courses, browse, then self-allocate to small teaching activities / tutorials so they can better plan their time. Find out more on the Timetable webpage.
Class summaries, if available, can be accessed by clicking on the View link for the relevant class number.
Second Semester
| Class number | Class start date | Last day to enrol | Census date | Class end date | Mode Of Delivery | Class Summary |
|---|---|---|---|---|---|---|
| 5780 | 25 Jul 2022 | 01 Aug 2022 | 31 Aug 2022 | 28 Oct 2022 | In Person | View |