Most textual entailment models focus on lexical gaps between the premise text and the hypothesis, but rarely on knowledge gaps. We focus on filling these knowledge gaps in the Science Entailment task, by leveraging an external structured knowledge base (KB) of science facts. Our new architecture combines standard neural entailment models with a knowledge lookup module. To facilitate this lookup, we propose a fact-level decomposition of the hypothesis, and verifying the resulting sub-facts against both the textual premise and the structured KB. Our model, NSnet, learns to aggregate predictions from these heterogeneous data formats. On the SciTail dataset, NSnet outperforms a simpler combination of the two predictions by 3% and the base entailment model by 5%.

We propose KDSL, a new word sense disambiguation (WSD) framework that utilizes knowledge to automatically generate sense-labeled data for supervised learning. First, from WordNet, we automatically construct a semantic knowledge base called DisDict, which provides refined feature words that highlight the differences among word senses, i.e., synsets. Second, we automatically generate new sense-labeled data by DisDict from unlabeled corpora. Third, these generated data, together with manually labeled data and unlabeled data, are fed to a neural framework conducting supervised and unsupervised learning jointly to model the semantic relations among synsets, feature words and their contexts. The experimental results show that KDSL outperforms several representative state-of-the-art methods on various major benchmarks. Interestingly, it performs relatively well even when manually labeled data is unavailable, thus provides a potential solution for similar tasks in a lack of manual annotations.

We aim to predict whether a primary school student will perform in the `below standard' band of a national standardized test. We exploit a data set containing test performance on the National Assessment Program - Literacy and Numeracy (NAPLAN); a test given annually to all Australian school students in grades 3, 5, 7, and 9. We separate the analysis into students in grade 5 and above, for which previous achievement may be used as a predictor; and students in grade 3, which must rely on family- and school-level predictors only. We train and compare a set of classifiers for reading and numeracy learning areas respectively. The classifiers achieve good predictive power in terms of area under the ROC curve, suggesting that it is feasible for schools to more accurately screen a large number of students for academic risk.

Distant supervision is a popular method for performing relation extraction from text that is known to produce noisy labels. Most progress in relation extraction and classification has been made with crowdsourced corrections to distant-supervised labels, and there is evidence that indicates still more would be better. In this paper, we explore the problem of propagating human annotation signals gathered for open-domain relation classification through the CrowdTruth methodology for crowdsourcing, that captures ambiguity in annotations by measuring inter-annotator disagreement. Our approach propagates annotations to sentences that are similar in a low dimensional embedding space, expanding the number of labels by two orders of magnitude. Our experiments show significant improvement in a sentence-level multi-class relation classifier.

A machine-learning study that analysed hundreds of thousands of earthquakes beat the standard method at predicting the location of aftershocks. Scientists say that the work provides a fresh way of exploring how changes in ground stress, such as those that occur during a big earthquake, trigger the quakes that follow. It could also help researchers to develop new methods for assessing seismic risk. "We've really just scratched the surface of what machine learning may be able to do for aftershock forecasting," says Phoebe DeVries, a seismologist at Harvard University in Cambridge, Massachusetts. She and her colleagues report their findings on 29 August in Nature.

Scientists have used artificial intelligence to predict how cancers will progress and evolve, which could help doctors design the most effective treatment for each patient. A team led by the Institute of Cancer Research, London (ICR) and the University of Edinburgh developed a new technique known as Revolver (Repeated evolution of cancer), which picks out patterns in DNA mutation within cancers and uses the information to forecast future genetic changes. They said the ever-changing nature of tumours is one of the biggest challenges of treatment – with cancers often evolving to a drug-resistant form. Parents think children should be taught signs of cancer, poll finds'Exciting' cancer drug combination shrinks tumours and stops growth Parental cancer has lifetime impact on children's education and earnin Vaping causes DNA mutations which could lead to cancer, says study'Exciting' cancer drug combination shrinks tumours and stops growth Parental cancer has lifetime impact on children's education and earnin But if doctors can predict how a tumour will evolve, they could intervene earlier to stop cancer in its tracks before it has had a chance to evolve or develop resistance, increasing the patient's chances of survival. The team also found a link between certain sequences of repeated tumour mutations and survival outcome.

SYDNEY – A robot submarine able to hunt and kill the predatory crown-of-thorns starfish that are devastating the Great Barrier Reef was unveiled by Australian researchers on Friday. Scientists at Queensland University of Technology (QUT) said the robot, named the RangerBot and developed with a grant from Google, would serve as a "robo reef protector" for the vast World Heritage site off Australia's northeastern coast. The RangerBot has an eight-hour battery life and computer vision capabilities allowing it to monitor and map reef areas at scales not previously possible. "RangerBot is the world's first underwater robotic system designed specifically for coral reef environments, using only robot-vision for real-time navigation, obstacle avoidance and complex science missions," said Matthew Dunbabin, the QUT professor who unveiled the submarine. "This multifunction ocean drone can monitor a wide range of issues facing coral reefs including coral bleaching, water quality, pest species, pollution and siltation."

Whether you do your shopping online or in store, your retail experience is the latest battleground for the artificial intelligence (AI) and machine learning revolution. Major Australian retailers have begun to realise they have a lot to gain from getting their AI strategy right, with Woolworths currently openly recruiting a head of AI and machine learning to be supported by a team of data scientists. The newly developed Woolworths division WooliesX aims to bring together a diverse group of teams -- including technology, customer digital experience, e-commerce, financial services and digital customer experience. To understand the opportunities and threats for all major retailers, it's useful to understand why artificial intelligence is back on the agenda. Two crucial things have changed since the initial forays into AI decades ago: data and computing power.

Internet-connected robots that can stream audio and video are increasingly helping housebound sick children and elderly people keep in touch with teachers, family and friends, combating the scourge of isolation and loneliness. Zoe Johnson, 16, hasn't been to school since she was 12. She went to the doctor in 2014 "with a bit of a sore throat", and "somehow that became A&E [accident and emergency]," says her mother, Rachel Johnson. The doctors diagnosed myalgic encephalomyelitis, ME for short, also known as Chronic Fatigue Syndrome - a debilitating illness affecting the nervous and immune systems. Zoe missed a lot of school but was able to continue with her studies with the help of an online tutor.

H2O.ai, the open source leader in AI, today announced an expanded presence in Australia due to heightened demand from customers for automatic machine learning and data science solutions in the region. As part of its efforts to democratize AI globally, H2O.ai will sponsor and attend the NVIDIA AI Conference in Sydney next week, as well as host a meetup to engage with data science professionals about its award-winning machine learning platforms and provide Australian businesses with the power of scalable AI. H2O.ai's expanded efforts in Australia come at a time of unprecedented demand for its products in the region. Since its launch in late 2017, the company's automatic machine learning platform Driverless AI has been deployed by customers, including Stanley Black and Decker, Armada Health, Deserve, G5 and more. NVIDIA AI Conference is a premier event on artificial intelligence and deep learning, and showcases the latest breakthroughs from universities, startups and major enterprises in a wide range of fields such as smart cities, autonomous machines, virtual reality and more.