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 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. Obtain documents from various sources and transform them into a common XML or RDF format with a knowledge of SAX and XPATH.
• 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.

Two written assignments with programming option (40%), written final exam (60%).

Thirty one-hour lectures and six two hour tutorial/laboratory sessions.

12 units of 3000 series IT courses including COMP3410 or COMP3420 and 6 units of MATH/STAT courses or COMP2600.

see requisite statement

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.