ARC Research Network for Enabling Human Communication. The Human Communication Network promotes interdisciplinary research in speech, language, and sound by and between humans and machines. The network connects leading and emerging researchers across disciplines, exploits previously unrecognised intersections, supports interdisciplinary graduate training and exchanges, provides database storage infrastructure, and consults with industry and government to set, not follow, research agendas. By ge ....ARC Research Network for Enabling Human Communication. The Human Communication Network promotes interdisciplinary research in speech, language, and sound by and between humans and machines. The network connects leading and emerging researchers across disciplines, exploits previously unrecognised intersections, supports interdisciplinary graduate training and exchanges, provides database storage infrastructure, and consults with industry and government to set, not follow, research agendas. By generating an explosion of new approaches and knowledge, the network will build Australia's reputation as a leader in communication science and technology via advances in automatic speech recognition, distress call monitoring, hearing prostheses, web interfaces, and data retrieval and data mining systems.Read moreRead less
Concept-based retrieval and interpretation for large data sets. Access to on-line information is growing at an exponential rate, fuelled by advances in computing and
communications technologies. Current information retrieval methods are becoming ineffective due to
their reliance on simple term-based methods, resulting in a massive number of matches, of which only
a small proportion are relevant. We address this problem by developing new matching algorithms which
understand the underlying ....Concept-based retrieval and interpretation for large data sets. Access to on-line information is growing at an exponential rate, fuelled by advances in computing and
communications technologies. Current information retrieval methods are becoming ineffective due to
their reliance on simple term-based methods, resulting in a massive number of matches, of which only
a small proportion are relevant. We address this problem by developing new matching algorithms which
understand the underlying meaning of documents in database repositories - by building semantic
structures semi-automatically - and thus provide more relevant information to queries.
This project will be of great benefit to a multitude of end-users in medicine, history, law and many other disciplines.
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