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Field of Research : Artificial Intelligence and Image Processing
Research Topic : Linguistics
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  • Funded Activity

    Discovery Projects - Grant ID: DP0450750

    Funder
    Australian Research Council
    Funding Amount
    $290,000.00
    Summary
    A scalable and portable question-answering system. The current availability of large volumes of free text digitally stored demands the development of methodologies that can automatically find specific answers to user questions about this "unstructured" information. The goal of this project is to develop a scalable portable and domain-independent real-time natural-language question-answering system that explores the logical contents of the text. To achieve this we will fuse current approaches to .... A scalable and portable question-answering system. The current availability of large volumes of free text digitally stored demands the development of methodologies that can automatically find specific answers to user questions about this "unstructured" information. The goal of this project is to develop a scalable portable and domain-independent real-time natural-language question-answering system that explores the logical contents of the text. To achieve this we will fuse current approaches to question answering with approaches that look at the logical contents of the questions and answer candidates. A central part of the project will be the characterisation of the optimal logical forms, the determination of efficient methods to create and store sentence logical forms of potentially large volumes of text, and the treatment of difficult questions by incorporating summarisation and text generation techniques.
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    Funded Activity

    Discovery Projects - Grant ID: DP0449928

    Funder
    Australian Research Council
    Funding Amount
    $135,000.00
    Summary
    A Layered Controlled Natural Language for Knowledge Representation. In this research project we will develop a controlled natural language for knowledge representation that has the potential to bridge the gap between fragments of natural language and formal languages. This controlled language will be based on a variety of increasing sophisticated layers, each building upon those below it by providing enhancements in expressive power. Sentences of the controlled language will be unambiguously tra .... A Layered Controlled Natural Language for Knowledge Representation. In this research project we will develop a controlled natural language for knowledge representation that has the potential to bridge the gap between fragments of natural language and formal languages. This controlled language will be based on a variety of increasing sophisticated layers, each building upon those below it by providing enhancements in expressive power. Sentences of the controlled language will be unambiguously translatable into a corresponding formal language. Anyone who can read and write English can immediately use the controlled language with the help an intelligent text editor. This technology will make it possible for non-specialists to write problem specifications in terms of the application domain without the need to formally encode the information.
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    Funded Activity

    Discovery Projects - Grant ID: DP160102156

    Funder
    Australian Research Council
    Funding Amount
    $388,000.00
    Summary
    Improved syntactic and semantic analysis for natural language processing. This project aims to improve the accuracy of syntactic and semantic analysis of natural language for automatic extraction of meaning from text. Many data mining and information extraction applications rely on syntactic and semantic analysis. Current analysis approaches are limited because they require expensive manually-labelled data. The project plans to develop new indirectly-supervised approaches to overcome this labell .... Improved syntactic and semantic analysis for natural language processing. This project aims to improve the accuracy of syntactic and semantic analysis of natural language for automatic extraction of meaning from text. Many data mining and information extraction applications rely on syntactic and semantic analysis. Current analysis approaches are limited because they require expensive manually-labelled data. The project plans to develop new indirectly-supervised approaches to overcome this labelled data bottleneck. By integrating information from large text corpora and structured databases, the project aims to minimise the reliance on manually-labelled data for training natural language processing systems. Automatic methods for syntactic and semantic analysis would have a wide range of applications in extracting information from large collections of unstructured data, such as hospital patient records or social media.
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    Funded Activity

    Discovery Projects - Grant ID: DP1097291

    Funder
    Australian Research Council
    Funding Amount
    $362,000.00
    Summary
    Parsing the web: Exploiting redundancy to understand language. This project will automatically learn the grammatical structure of language by exploiting redundancy of facts, like 'Mozart was born in 1756', from a trillion words of web text. These facts will be used to understand more complex sentences. This will enable smart information use of text with grammatical information for large-scale information access for the first time. This project will strengthen Australia's world-class expertise, .... Parsing the web: Exploiting redundancy to understand language. This project will automatically learn the grammatical structure of language by exploiting redundancy of facts, like 'Mozart was born in 1756', from a trillion words of web text. These facts will be used to understand more complex sentences. This will enable smart information use of text with grammatical information for large-scale information access for the first time. This project will strengthen Australia's world-class expertise, providing opportunities for future researchers in this area. Our expanded C&C tools and trillion word corpus will be used by academics, companies and governments, in Australia and internationally, aiding applications including financial surveillance and fraud detection.
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    Funded Activity

    Discovery Early Career Researcher Award - Grant ID: DE120102900

    Funder
    Australian Research Council
    Funding Amount
    $375,000.00
    Summary
    WikiLinks: web-scale linking and fact extraction with Wikipedia. Wikipedia is the most popular web site for finding facts, but articles about local or specialist topics are often missing or unreliable. WikiLinks will use artificial intelligence to link names in text to corresponding Wikipedia articles, allowing us to automatically create and augment Wikipedia content by summarising existing material on the web.
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