Incremental syntactic parsing and coreference resolution. As computers become smaller, keyboards and screens become increasingly impractical. We'd like to be able to talk to our computers, but they'd have to understand what we say. This project will develop a computational model that tracks which things are talked about and identifies 'who did what to whom' in text or speech.
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.
Fairness in Natural Language Processing. Natural language processing (NLP) has achieved spectacular commercial successes in recent years, and has been deployed across an ever-increasing breadth of devices and application areas. At the same time, there has been stark evidence to indicate that naively-trained models amplify biases in training data, and perform inconsistently across text relating to different demographic groupings of individuals. This project aims to systematically quantify the ext ....Fairness in Natural Language Processing. Natural language processing (NLP) has achieved spectacular commercial successes in recent years, and has been deployed across an ever-increasing breadth of devices and application areas. At the same time, there has been stark evidence to indicate that naively-trained models amplify biases in training data, and perform inconsistently across text relating to different demographic groupings of individuals. This project aims to systematically quantify the extent of such biases, and develop models that are both more socially equitable, as well as less prone to expose private data in the learned representations. In doing so, it will make NLP more accessible to new populations of users, and remove socio-technological barriers to NLP uptake.Read moreRead less
Perceptually-motivated speech parameters for concurrent coding and noise-robust distributed recognition of human speech for mobile telephony systems. With speech being a simple and natural form of communication, speech recognition technology is being widely used in mobile phones. Nowadays, consumers can interact with remote systems via spoken words. This project will develop remote speech recognition with better accuracy and noise-robustness while using the existing mobile phone infrastructure.
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.
Computational models of synergies in human language acquisition. How do children learn language? Do they first learn to recognise words and then associate words with meanings, or do they use the meanings to figure out what the words are, or do they do both at the same time, and if so, how? This project will investigate questions like these using advanced computational models of the way children learn from their environment.
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.Read moreRead less
Language engineering in the field: preserving 100 endangered languages in New Guinea. Efforts to preserve the world's endangered linguistic heritage are labour-intensive, and unable to keep up with the pace of language loss. This project investigates a new approach to language preservation, using techniques from language engineering, and leveraging the labour of mother-tongue speakers.
Improving human reasoning with causal Bayes networks: a multimodal approach. This project aims to improve human causal and probabilistic reasoning about complex systems by taking a user-centric, multimodal, interactive approach. The project will explore new integrated visual and verbal ways of explaining a causal probabilistic model and its reasoning, to reduce known human reasoning difficulties, and investigate how to reduce cognitive load by prioritising the most useful user- and context-speci ....Improving human reasoning with causal Bayes networks: a multimodal approach. This project aims to improve human causal and probabilistic reasoning about complex systems by taking a user-centric, multimodal, interactive approach. The project will explore new integrated visual and verbal ways of explaining a causal probabilistic model and its reasoning, to reduce known human reasoning difficulties, and investigate how to reduce cognitive load by prioritising the most useful user- and context-specific information. Expected outcomes include novel AI methods that empower users to drive the reasoning process and strengthen trust in the system’s reasoning. Performance will be assessed in medical and legal domains, with significant potential benefits to end users from better, more transparent reasoning and decision making.Read moreRead less
Biochemical text mining for advancing chemical and pharmaceutical knowledge. The project aims to develop novel natural language processing methods to find, extract and structure complex chemical reaction information in scientific literature. The project addresses a recognised bottleneck to efficiency in the drug discovery process, by enabling biochemical research results to be turned into actionable information. This has the potential to inform and accelerate development of effective drug treatm ....Biochemical text mining for advancing chemical and pharmaceutical knowledge. The project aims to develop novel natural language processing methods to find, extract and structure complex chemical reaction information in scientific literature. The project addresses a recognised bottleneck to efficiency in the drug discovery process, by enabling biochemical research results to be turned into actionable information. This has the potential to inform and accelerate development of effective drug treatments through the linking of relevant biochemical information. By delivering new methods that improve the compilation of knowledge about chemicals and drugs from textual information resources, the project hopes to enable faster drug discovery.Read moreRead less