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Lecturer(s)
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Hájek Petr, prof. Ing. Ph.D.
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Course content
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The role and specifics of unstructured data Possibilies of inverted index design Dictionary-based models Statistical approaches to unstructured data extraction Relation extraction from unstructured data Semantic annotation and ontologies Visual and text information extraction from images Models for information retrieval from images Models for automatic speech recognition Evaluation of quality of unstructured data processing
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Learning activities and teaching methods
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Monologic (reading, lecture, briefing), Dialogic (discussion, interview, brainstorming), Work with text (with textbook, with book), Methods of individual activities, Laboratory work
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Learning outcomes
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The subject aims to develop a general understanding of the fundamental methods for unstructured data processing, in particular text documents, image and audio data. This processing leads to structured or at least semi-structured data. This enables further analyses, including data visualisation and knowledge discovery.
Students will be capable of understanding both theoretical and practical aspects of unstructured data processing and information retrieval in these data. They will also be able of designing systems for automatic unstructured data processing.
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Prerequisites
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Basic skills in PC and MS Excel utilization.
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Assessment methods and criteria
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Oral examination, Systematic monitoring
Assignment: successful elaboration of given tasks with 60% at minimum. Successful defense of a practical project that cover theoretical knowledge gained within this course and includes a design of system for automatic processing of selected set of unstructured data. Examination: oral examination. Detailed information will be provided during the first lecture and in Stag.
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Recommended literature
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AUGER, A., BARRI?RE, C. Pattern-based Approaches to Semantic Relation Extraction: A State-of-the-art.. 2008.
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BOULTON, D., HAMMERSLEY, M. Analysis of Unstructured Data. London, 2006.
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DATTA, R., JOSHI, D., LI, J., WANG, J. Z. Image Retrieval: Ideas, Influences, and Trends of the New Age. 2008.
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GRIMM, M., KROSCHEL, K. Robust Speech Recognition and Understanding. 2007.
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HEATH, T., BIZER, CH. Linked Data: Evolving the Web into a Global Data Space. 2011.
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MANNING, C. D. Foundations of Statistical Natural Language Processing. Cambridge, 1999.
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MANNING, CH. D., RAGHAVAN, P., SCHUTZE, H. Introduction to Information Retrieval. Cambridge, 2008.
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MINER, G. Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications. Amsterdam, 2012.
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