ORCID Profile
0000-0003-0579-8018
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Research Topics
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Top 5 Research Topics
ANZSRC Field of Research (FoR)
Operations Research | Simulation and Modelling | Artificial Intelligence and Image Processing | Natural Language Processing
ANZSRC Socio-Economic Objective (SEO)
Expanding Knowledge in the Information and Computing Sciences | Logistics | Manufacturing not elsewhere classified |
Related Links
Publications
CommViz: Visualization of semantic patterns in large social communication networks
Publisher: SAGE Publications
Date: 02-2017
Abstract: We introduce CommViz, an information visualization tool that enables complex communication networks to be explored, exposing trends and patterns in the data at a glance. We adapt a visualization approach known as hive plots to reflect the semantic structure of the networks, a generalization we call semantic hive plots. The method efficiently organizes and provides insight into complex, high-dimensional communication data such as email and messages on social media. We present the architecture of the CommViz tool and its application to the Enron email corpus as a case study, demonstrating how the structure of the visualization enables investigation of patterns and relationships in a large set of messages. We also provide a user study performed with Amazon Mechanical Turk that shows the value of the tool for certain complex data interrogations and further show how the incorporation of semantic structure on semantic coordinates can also be applied to parallel coordinates visualization. The integration of the social network characteristics with semantic attributes of the underlying data in a single visualization is, to our knowledge, a novel contribution of the work. The tool can be accessed at commviz.eng.unimelb.edu.au . Code is available at eadbiomed/commviz . The Enron email corpus is available from nron_email.html .
Data Mining in Elite Sports: A Review and a Framework
Publisher: Informa UK Limited
Date: 07-2013
Detecting Phishing Emails Using Hybrid Features
Publisher: IEEE
Date: 2009
The impact of frame semantic annotation levels, frame-alignment techniques, and fusion methods on factoid answer processing
Publisher: Wiley
Date: 06-11-2009
DOI: 10.1002/ASI.20989
Structure Elucidation at the Nanomole Scale. 1. Trisoxazole Macrolides and Thiazole-Containing Cyclic Peptides from the Nudibranch Hexabranchus sanguineus
Publisher: American Chemical Society (ACS)
Date: 02-03-2009
DOI: 10.1021/NP8007649
Linked Data Triples Enhance Document Relevance Classification
Publisher: MDPI AG
Date: 20-07-2021
DOI: 10.3390/APP11146636
Abstract: Standardized approaches to relevance classification in information retrieval use generative statistical models to identify the presence or absence of certain topics that might make a document relevant to the searcher. These approaches have been used to better predict relevance on the basis of what the document is “about”, rather than a simple-minded analysis of the bag of words contained within the document. In more recent times, this idea has been extended by using pre-trained deep learning models and text representations, such as GloVe or BERT. These use an external corpus as a knowledge-base that conditions the model to help predict what a document is about. This paper adopts a hybrid approach that leverages the structure of knowledge embedded in a corpus. In particular, the paper reports on experiments where linked data triples (subject-predicate-object), constructed from natural language elements are derived from deep learning. These are evaluated as additional latent semantic features for a relevant document classifier in a customized news-feed website. The research is a synthesis of current thinking in deep learning models in NLP and information retrieval and the predicate structure used in semantic web research. Our experiments indicate that linked data triples increased the F-score of the baseline GloVe representations by 6% and show significant improvement over state-of-the art models, like BERT. The findings are tested and empirically validated on an experimental dataset and on two standardized pre-classified news sources, namely the Reuters and 20 News groups datasets.
The impact of semantic class identification and semantic role labeling on natural language answer extraction
Publisher: Springer Berlin Heidelberg
Date: 2008
Performance analysis and prediction in triathlon
Publisher: Informa UK Limited
Date: 16-07-2016
DOI: 10.1080/02640414.2015.1065341
Abstract: Performance in triathlon is dependent upon factors that include somatotype, physiological capacity, technical proficiency and race strategy. Given the multidisciplinary nature of triathlon and the interaction between each of the three race components, the identification of target split times that can be used to inform the design of training plans and race pacing strategies is a complex task. The present study uses machine learning techniques to analyse a large database of performances in Olympic distance triathlons (2008-2012). The analysis reveals patterns of performance in five components of triathlon (three race "legs" and two transitions) and the complex relationships between performance in each component and overall performance in a race. The results provide three perspectives on the relationship between performance in each component of triathlon and the final placing in a race. These perspectives allow the identification of target split times that are required to achieve a certain final place in a race and the opportunity to make evidence-based decisions about race tactics in order to optimise performance.
Can shallow semantic class information help answer passage retrieval?
Publisher: Springer Berlin Heidelberg
Date: 2009
FrameNet-based fact-seeking answer processing: A study of semantic alignment techniques and lexical coverage
Publisher: Springer Berlin Heidelberg
Date: 2008
Novel weighting in single hidden layer feedforward neural networks for data classification
Publisher: Elsevier BV
Date: 07-2012
Supporting athlete selection and strategic planning in track cycling omnium: A statistical and machine learning approach
Publisher: Elsevier BV
Date: 06-2013
Textual emotion classification: An interoperability study on cross-genre data sets
Publisher: Springer International Publishing
Date: 2017
Semantic Triple-Assisted Learning for Question Answering Passage Re-ranking
Publisher: Springer Nature Switzerland
Date: 2023
Learning parse-free event-based features for textual entailment recognition
Publisher: Springer Berlin Heidelberg
Date: 2010
Probabilistic modelling to give advice about rowing split measures to support strategy and pacing in race planning
Publisher: Informa UK Limited
Date: 08-2011
Modelling and analysing track cycling Omnium performances using statistical and machine learning techniques
Publisher: Informa UK Limited
Date: 05-2013
DOI: 10.1080/02640414.2012.757344
Abstract: This article describes the utilisation of an unsupervised machine learning technique and statistical approaches (e.g., the Kolmogorov-Smirnov test) that assist cycling experts in the crucial decision-making processes for athlete selection, training, and strategic planning in the track cycling Omnium. The Omnium is a multi-event competition that will be included in the summer Olympic Games for the first time in 2012. Presently, selectors and cycling coaches make decisions based on experience and intuition. They rarely have access to objective data. We analysed both the old five-event (first raced internationally in 2007) and new six-event (first raced internationally in 2011) Omniums and found that the addition of the elimination race component to the Omnium has, contrary to expectations, not favoured track endurance riders. We analysed the Omnium data and also determined the inter-relationships between different in idual events as well as between those events and the final standings of riders. In further analysis, we found that there is no maximum ranking (poorest performance) in each in idual event that riders can afford whilst still winning a medal. We also found the required times for riders to finish the timed components that are necessary for medal winning. The results of this study consider the scoring system of the Omnium and inform decision-making toward successful participation in future major Omnium competitions.
Data Envelopment Analysis of linguistic features and passage relevance for open-domain Question Answering
Publisher: Elsevier BV
Date: 05-2022
A machine learning approach to predicting winning patterns in track cycling omnium
Publisher: Springer Berlin Heidelberg
Date: 2010
Team technical performance characteristics and their association with match outcome in elite netball
Publisher: Informa UK Limited
Date: 08-06-2021
Related Organisations
Islamic Azad University Science And Research Branch
Location: Iran (Islamic Republic of)
Islamic Azad University Tehran North Branch
Location: Iran (Islamic Republic of)
Related Funding Activities
Discovery Projects - Grant ID: DP220101925
Start Date: 06-2022
End Date: 06-2025
Amount: $480,000.00
Funder: Australian Research Council
View Funded Activity