Processing of semi-structured documents such as internet pages, Really Simple Syndication (RSS) feeds and their accompanying news items, and Portable Document Format (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: 1. Information Retrieval (IR), 2. Natural Language Processing (NLP), 3. Machine/Deep Learning (M/DL) for documents, and 4. relevant tools for the Web. Basic tasks here are covered, including content collection and extraction, formal and informal NLP, Information Extraction (IE), IR, 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.(The older version: This course considers the “document” and its various genres as a fundamental object for business, government and community, such as web pages, social media feeds, news items, and PDF brochures. The goal is to introduce concepts and hands-on tools for automated understanding of large amounts of text. 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. Tasks include 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 great depth.)
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Requisite and Incompatibility
To enrol in this course you must be enrolled in VCOMP OR have completed 6 units of: (COMP6240/2400 OR COMP6260/1600 OR COMP6442/2100) AND have completed 6 units of: (COMP6730/1730 OR COMP6710/1110). Incompatible with COMP4650 and COMP6990.
Referenced entries (7)
Links and code mentions retain their original context above. They may describe options, prerequisites or exclusions.
- COMP6240 · 2027
- COMP6260 · 2027
- COMP6442 · 2027
- COMP6730 · 2027
- COMP6710 · 2027
- COMP4650 · 2027 · saved source not linked
- COMP6990 · 2027 · saved source not linked
Learning Outcomes
Upon successful completion, students will have the knowledge and skills to:
Differentiate between the probabilistic theories of language, document structure, and their applications to text generation and analysis, from the pioneering papers to the current advances in research and practice.
Apply, analyse, evaluate, and extend methods for natural language processing, word representation, document feature engineering, information retrieval, text classification, text clustering, and language modelling.
Create automated workflows for document analysis, information retrieval, and text classification tasks by developing and deploying typical algorithms, code libraries, and software (e.g., for natural language processing and machine/deep learning).
Demonstrate advanced proficiency at designing, justifying, conducting, and reporting on such analysis and evaluation experiments at document and corpus levels.
Published offerings
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Complete offering text
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. There are no current offerings for this course.
Source evidence (1 saved copy)
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