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    Developing Corpora for Statistical Graphical Language Models


    O'Sullivan, Andrew and Keyes, Laura and Winstanley, Adam C. (2006) Developing Corpora for Statistical Graphical Language Models. In: International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV'06), 26-29 June 2006, Las Vegas.

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    Abstract

    In this work Statistical Graphical Language Models (SGLMs), a technique adapted from Statistical Language Models (SLMs), are applied to the task of graphical object recognition. SLMs are used in Natural Language Processing for tasks such as Speech Recognition and Information Retrieval. SGLMs view graphical objects as belonging to graphical languages and use this view to compute probabilistic distributions of graphical objects within graphical documents. SGLMs such as N-grams require large corpora of training data, which consist of graphical objects in contextual use (real world graphical documents). Constructing corpora is an important stage in developing the models and many issues need to be addressed. This paper discusses the development of graphical corpora and presents approaches to some of the problems encountered.

    Item Type: Conference or Workshop Item (Paper)
    Keywords: Graphics Recognition; Statistical Language Modelling; Corpora;
    Academic Unit: Faculty of Science and Engineering > Computer Science
    Item ID: 4915
    Depositing User: Dr. Adam Winstanley
    Date Deposited: 25 Apr 2014 15:38
    Refereed: No
    URI:

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