Making human place knowledge digestible by computers. This project aims to develop the tools that will enable people to interact intuitively with computers about places and the relations between places. People understand their environment in a different way to computers; they think of places and their relations, while computers use coordinates and maps. People’s interaction with maps is cognitively costly and error-prone, which is becoming untenable in situations needing time-critical decision m ....Making human place knowledge digestible by computers. This project aims to develop the tools that will enable people to interact intuitively with computers about places and the relations between places. People understand their environment in a different way to computers; they think of places and their relations, while computers use coordinates and maps. People’s interaction with maps is cognitively costly and error-prone, which is becoming untenable in situations needing time-critical decision making. The project will revolutionise the design of information services where computers deal with humans and location in time-critical or stressful situations, including emergency calls, disaster response and local search queries. The uptake of this design by industry will lead to economic benefits as well as a safer society living in a smarter environment.Read moreRead less
Learning Deep Semantics for Automatic Translation between Human Languages. This project seeks to integrate deep linguistics and deep learning to improve translation quality. The modern world relies increasingly on automatic translation of human languages to deal with billions of documents. Current translation systems struggle with complex texts and often produce misleading or incoherent outputs. Furthermore, they translate sentences independently and ignore their overall document-wide context. T ....Learning Deep Semantics for Automatic Translation between Human Languages. This project seeks to integrate deep linguistics and deep learning to improve translation quality. The modern world relies increasingly on automatic translation of human languages to deal with billions of documents. Current translation systems struggle with complex texts and often produce misleading or incoherent outputs. Furthermore, they translate sentences independently and ignore their overall document-wide context. This project seeks to address these issues by developing a new approach using semantics – the underlying meaning of the text – to drive translation, both as discrete structures and continuous representations learned via deep learning. This may improve translation quality, thereby improving automatic translation for end-users.Read moreRead less
Personalised topic modelling and sentiment analysis for enhanced information discovery over document streams. This project will develop personalised information discovery, navigation and management systems of online content for the creative industries, e.g. to help advertising agencies understand market trends, and enable designers to discover and analyse information relating to new product concepts.
CyberMate: using digital social media and Internet data to support mental health interventions in young Australians. This project will develop CyberMate, which is a novel automated psychological intervention based on data collected from social networks, personal diaries, natural language processing and machine learning techniques. The Internet-based intervention will be the first of its kind, helping young people affected by depression and other mental health issues.
Information access through web-scale question-answer pair finding, ranking and matching. This project will aim to take web search to a new level of sophistication in accepting queries in the form of complex natural language questions, and returning a ranked list of natural language answers automatically extracted from a broad range of web user forums.
Beyond the grammar checker: automated copy-editing assistance. In the traditional publishing process, copy-editors correct and polish what authors write, but financial pressures mean that copy-editing is often considered a luxury. This project uses natural language processing and artificial intelligence techniques to develop technology that automates a significant proportion of the copy editing task.
Discovery Early Career Researcher Award - Grant ID: De120102900
Funder
Australian Research Council
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.
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.
Responding to requests and situations in assistive computer systems - a decision-theoretic approach. This project aims to enable computer agents to respond appropriately to people's spoken requests and circumstances (e.g., ask questions or perform actions). This project will investigate computational models for response generation, which will be implemented in assistive computer systems, thus enabling people to interact more easily with these systems.
Towards realistic verbal interactions between people and computers-a probabilistic approach. This project aims to facilitate natural spoken interactions between people and computer systems, addressing obstacles to the acceptance of these systems. We will investigate computational models for relevant aspects of spoken dialogue, which will be implemented in computer systems for diverse tasks (for example, home devices and phone-enabled services).