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Field of Research : Text Processing
Scheme : Discovery Projects
Australian State/Territory : NSW
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  • Funded Activity

    Discovery Projects - Grant ID: DP1095443

    Funder
    Australian Research Council
    Funding Amount
    $425,000.00
    Summary
    Natural Language Generation for Aboriginal Languages. Australian Aboriginal languages have a number of interesting characteristics that make them a challenge for language technology applications; as yet, there are none, unlike for the indigenous Inuit peoples of Canada and Maori of New Zealand. We will carry out a large-scale computational linguistic investigation of an Aboriginal language to create a data-to-text natural language generation system. The system will use data from Australian Rul .... Natural Language Generation for Aboriginal Languages. Australian Aboriginal languages have a number of interesting characteristics that make them a challenge for language technology applications; as yet, there are none, unlike for the indigenous Inuit peoples of Canada and Maori of New Zealand. We will carry out a large-scale computational linguistic investigation of an Aboriginal language to create a data-to-text natural language generation system. The system will use data from Australian Rules Football to automatically construct articles based on the data. This study of computational linguistics will have further national benefits through engagement of the owners of the language in the language survey, as well as generating articles that will encourage literacy and language maintenance.
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    Funded Activity

    Discovery Projects - Grant ID: DP0453131

    Funder
    Australian Research Council
    Funding Amount
    $330,000.00
    Summary
    Ask the Net: Intelligent Natural Language Learning. Natural Language Processing (NLP) has progressed rapidly using corpus-based machine learning techniques. However, corpus development costs cause a ?data bottleneck? which prevents systems from reaching human competence. This project overcomes the difficulties of creating huge corpora by employing the innate language ability of untrained contributors. We will show how to automatically select and present examples, containing informative lingui .... Ask the Net: Intelligent Natural Language Learning. Natural Language Processing (NLP) has progressed rapidly using corpus-based machine learning techniques. However, corpus development costs cause a ?data bottleneck? which prevents systems from reaching human competence. This project overcomes the difficulties of creating huge corpora by employing the innate language ability of untrained contributors. We will show how to automatically select and present examples, containing informative linguistic structures, which are most beneficial for training NLP systems. These examples will be analysed by many contributors whose responses will be automatically collated into corpora. Huge corpora are vital to emerging language technologies for managing textual information in the global economy.
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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: DP0346786

    Funder
    Australian Research Council
    Funding Amount
    $218,662.00
    Summary
    Incremental Knowledge Acquisition for Machine Translation from Multiple Experts. With increasing globalisation and an increasing amount of electronically available documents the need for machine translation is growing dramatically. The state-of-the-art in machine translation is still far from satisfactory. Substantial post-editing is necessary for most non-technical texts and even for many technical documents to make the translation really understandable. This project will develop a new approach .... Incremental Knowledge Acquisition for Machine Translation from Multiple Experts. With increasing globalisation and an increasing amount of electronically available documents the need for machine translation is growing dramatically. The state-of-the-art in machine translation is still far from satisfactory. Substantial post-editing is necessary for most non-technical texts and even for many technical documents to make the translation really understandable. This project will develop a new approach for buildingmachine translation systems by extending the unorthodox approach of Ripple-Down Rules, which proved very successful for building expert systems in the medical domain.It is intended to build a machine translation system by integrating the knowledge from many experts.
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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: DP0666600

    Funder
    Australian Research Council
    Funding Amount
    $336,000.00
    Summary
    An knowledge-based approach to multi-document text summarisation for automated meta-analysis of the scientific literature. The biomedical sciences produce literature at an exponential rate, and the size of this knowledge base far exceeds the capacity of humans to keep up with the growth in new knowledge. This project will develop computational text summarisation methods to abstract the content of scientific journal articles reporting clinical trials, and develop multi-document summarisation meth .... An knowledge-based approach to multi-document text summarisation for automated meta-analysis of the scientific literature. The biomedical sciences produce literature at an exponential rate, and the size of this knowledge base far exceeds the capacity of humans to keep up with the growth in new knowledge. This project will develop computational text summarisation methods to abstract the content of scientific journal articles reporting clinical trials, and develop multi-document summarisation methods to synthesise these abstracts using automated statistical meta-analysis methods. These methods have broad potential to improve text-summarisation technologies in general, to profoundly enhance our ability to integrate published knowledge, and to make a highly significant and specific contribution to improving the quality of evidence used in health decision-making.
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    Funded Activity

    Discovery Projects - Grant ID: DP0772252

    Funder
    Australian Research Council
    Funding Amount
    $282,935.00
    Summary
    A study of the potential for the public to be involved in the design of large scale public works. Public acceptability of infrastructure such as desalination plants or new public spaces, is a concern for the Australian Commonwealth and State Governments. However, tensions exist between the need for expedient planning and development of critical public infrastructure and Australian principles of democratic social and economic participation. The instrument developed by this research will inform pu .... A study of the potential for the public to be involved in the design of large scale public works. Public acceptability of infrastructure such as desalination plants or new public spaces, is a concern for the Australian Commonwealth and State Governments. However, tensions exist between the need for expedient planning and development of critical public infrastructure and Australian principles of democratic social and economic participation. The instrument developed by this research will inform public policy to negotiate and understand arrangements that balance social participation with Government objectives.
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    Funded Activity

    Discovery Projects - Grant ID: DP0665064

    Funder
    Australian Research Council
    Funding Amount
    $200,000.00
    Summary
    Using machine learning and automated document analysis methods to support English composition training. The project has the potential to further strengthen Australia's Higher Education industry by providing scalable solutions for teaching academic writing, a core competence for students in the tertiary sector. It is also a contribution to the E-learning industry in general. Innovating with these respective technologies will have a significant impact on the quality of online training. Australian .... Using machine learning and automated document analysis methods to support English composition training. The project has the potential to further strengthen Australia's Higher Education industry by providing scalable solutions for teaching academic writing, a core competence for students in the tertiary sector. It is also a contribution to the E-learning industry in general. Innovating with these respective technologies will have a significant impact on the quality of online training. Australian organizations will be able to reap the benefits of a well-tested platform, with the exclusive added capabilities that this project will develop. One of the software systems to be used in this project is an open source Learning Management System that is available to every Australian institution without any licensing cost.
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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 Projects - Grant ID: DP0665973

    Funder
    Australian Research Council
    Funding Amount
    $240,000.00
    Summary
    Exploring Scientific Information with Advanced New Search Tools. The rapidly growth of scientific literature in many fields makes finding information a challenge. For example, biologists produce over 1 million articles each year. Existing search tools have only limited success satisfying the demands of scientists' queries. This project will deliver intelligent e-research assistants capable of answering scientists' questions directly rather than returning a list of documents. This will allow scie .... Exploring Scientific Information with Advanced New Search Tools. The rapidly growth of scientific literature in many fields makes finding information a challenge. For example, biologists produce over 1 million articles each year. Existing search tools have only limited success satisfying the demands of scientists' queries. This project will deliver intelligent e-research assistants capable of answering scientists' questions directly rather than returning a list of documents. This will allow scientists to more efficiently exploit the literature enabling them to be more innovative and productive. This technology is applicable where ever finding facts in large volumes of text is critical, e.g. analysing surveillance material. Advanced search tools will have considerable academic and industrial impact.
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