ORCID Profile
0000-0002-7409-0948
Current Organisation
University of Western Australia
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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)
Language Studies | Translation and Interpretation Studies | Adaptive Agents and Intelligent Robotics | Comparative Language Studies | Other Artificial Intelligence | Infrastructure Engineering and Asset Management | Artificial Intelligence and Image Processing | Information Systems | Software Engineering | Geology | Interorganisational Information Systems | Programming Techniques | Resource geoscience | Mineralogy and crystallography | Simulation and Modelling | Exploration geochemistry | Engineering Practice | Interdisciplinary Engineering |
ANZSRC Socio-Economic Objective (SEO)
Productivity (excl. Public Sector) | Technological and organisational innovation | Computer Software and Services not elsewhere classified | Communication Across Languages and Culture | Computer software and services not elsewhere classified | Mining Machinery and Equipment | Expanding Knowledge in Engineering | Expanding Knowledge in the Information and Computing Sciences | Communication not elsewhere classified | Expanding Knowledge in Language, Communication and Culture
Related Links
Publications
Neural Representations of Natural Language
Publisher: Springer Singapore
Date: 2019
GeoDocA – Fast analysis of geological content in mineral exploration reports
Publisher: Elsevier BV
Date: 08-2019
Graph Embeddings for Non-IID Data Feature Representation Learning
Publisher: Springer Nature Singapore
Date: 2022
Introduction: Practical cognitive agents and robots
Publisher: Springer Science and Business Media LLC
Date: 12-03-2009
Internet collaboration and service composition as a loose form of teamwork
Publisher: Elsevier BV
Date: 08-2007
An Investigation of Neural Embeddings for Coreference Resolution
Publisher: Springer International Publishing
Date: 2015
HIN-RNN: A Graph Representation Learning Neural Network for Fraudster Group Detection With No Handcrafted Features
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2023
Lithium and Tamoxifen Modulate Behavior and Protein Kinase C Activity in the Animal Model of Mania Induced by Ouabain
Publisher: Oxford University Press (OUP)
Date: 07-08-2017
DOI: 10.1093/IJNP/PYX049
Towards Geological Knowledge Discovery Using Vector-Based Semantic Similarity
Publisher: Springer International Publishing
Date: 2018
Acquiring semantic relations using the web for constructing lightweight ontologies
Publisher: Springer Berlin Heidelberg
Date: 2009
An Incremental Anytime Algorithm for Mining T-Patterns from Event Streams
Publisher: Springer Singapore
Date: 2018
Defense-Friendly Images in Adversarial Attacks
Publisher: IEEE
Date: 2021
Constructing Web Corpora through Topical Web Partitioning for Term Recognition
Publisher: Springer Berlin Heidelberg
Date: 2008
Efficient Discovery of Recurrent Routine Behaviours in Smart Meter Time Series by Growing Subsequences
Publisher: Springer International Publishing
Date: 2015
Evaluation of three local descriptors on low resolution images for robot navigation
Publisher: IEEE
Date: 11-2009
CylE: Cylinder Embeddings for Multi-hop Reasoning over Knowledge Graphs
Publisher: Association for Computational Linguistics
Date: 2023
SConE: Simplified Cone Embeddings with Symbolic Operators for Complex Logical Queries
Publisher: Association for Computational Linguistics
Date: 2023
A Matrix-Vector Recurrent Unit Model for Capturing Compositional Semantics in Phrase Embeddings
Publisher: ACM
Date: 06-11-2017
Deep fusion of multimodal features for social media retweet time prediction
Publisher: Springer Science and Business Media LLC
Date: 24-10-2021
Web service clustering using text mining techniques
Publisher: Inderscience Publishers
Date: 2009
Spatio-Temporal Graph Representation Learning for Fraudster Group Detection
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
SubICap: Towards Subword-informed Image Captioning
Publisher: IEEE
Date: 2021
A Dual Network for Transfer Learning with Spike Train Data
Publisher: Springer International Publishing
Date: 2015
Auto-labelling entities in low-resource text
Publisher: Springer Science and Business Media LLC
Date: 15-01-2021
Constructing specialised corpora through analysing domain representativeness of websites
Publisher: Springer Science and Business Media LLC
Date: 02-03-2011
ICDM 2019 knowledge graph contest
Publisher: IEEE
Date: 11-2019
E-Service Composition Tools from a Lifecycle Perspective
Publisher: Springer Berlin Heidelberg
Date: 2007
Temporal Interaction Biased Community Detection in Social Networks
Publisher: Springer International Publishing
Date: 2016
Constructing consumer-oriented medical terminology from the web a supervised classifier ensemble approach
Publisher: Springer International Publishing
Date: 2014
Understanding ore-forming conditions using machine reading of text
Publisher: Elsevier BV
Date: 08-2021
Automatic semantic knowledge extraction from electronic forms
Publisher: SAGE Publications
Date: 23-05-2022
DOI: 10.1177/1748006X221098272
Abstract: Electronic tabular forms are an intuitive way for organisations to collect, present and store structured information for human readers. Forms use features such as fonts, colours and cell positioning to help readers navigate and find information. Millions of forms, typically in Portable Document Format (PDF), are generated by businesses as part of routine operations. Unlike human readers, machines are not able to directly ‘understand’ the implicit cues contained in the fonts, colours and use of boxes without explicit processing. In this paper, a supervised computer vision model is proposed to decompose the PDF form document into nested microtables. The cells within these microtables are then processed using a customisable rule bank for meaningful table content and semantic relationship extraction. The process is demonstrated on an industry dataset of 37 maintenance procedure documents containing 373 pages and 1016 unique microtables. A web application EMU (Extracting Machine Understandable Semantics from Forms) demonstrates how data captured in tables with different dimensions in procedural forms can be automatically extracted and stored in JavaScript Object Notation (JSON). Identifying and extracting nested tables is a critical fundamental step for future applications to support machine-automated search and extraction of data at scale for both maintenance and other procedural documentation.
How Well Sentence Embeddings Capture Meaning
Publisher: ACM
Date: 08-12-2015
Video Description
Publisher: Association for Computing Machinery (ACM)
Date: 16-10-2019
DOI: 10.1145/3355390
Abstract: Video description is the automatic generation of natural language sentences that describe the contents of a given video. It has applications in human-robot interaction, helping the visually impaired and video subtitling. The past few years have seen a surge of research in this area due to the unprecedented success of deep learning in computer vision and natural language processing. Numerous methods, datasets, and evaluation metrics have been proposed in the literature, calling the need for a comprehensive survey to focus research efforts in this flourishing new direction. This article fills the gap by surveying the state-of-the-art approaches with a focus on deep learning models comparing benchmark datasets in terms of their domains, number of classes, and repository size and identifying the pros and cons of various evaluation metrics, such as SPICE, CIDEr, ROUGE, BLEU, METEOR, and WMD. Classical video description approaches combined subject, object, and verb detection with template-based language models to generate sentences. However, the release of large datasets revealed that these methods cannot cope with the ersity in unconstrained open domain videos. Classical approaches were followed by a very short era of statistical methods that were soon replaced with deep learning, the current state-of-the-art in video description. Our survey shows that despite the fast-paced developments, video description research is still in its infancy due to the following reasons: Analysis of video description models is challenging, because it is difficult to ascertain the contributions towards accuracy or errors of the visual features and the adopted language model in the final description. Existing datasets neither contain adequate visual ersity nor complexity of linguistic structures. Finally, current evaluation metrics fall short of measuring the agreement between machine-generated descriptions with that of humans. We conclude our survey by listing promising future research directions.
Estimating Conceptual Similarities Using Distributed Representations and Extended Backpropagation
Publisher: Springer London
Date: 2012
A novel framework for constructing partially monotone rule ensembles
Publisher: IEEE
Date: 04-2018
DataDeps.jl: Repeatable Data Setup for Reproducible Data Science
Publisher: Ubiquity Press, Ltd.
Date: 29-10-2019
DOI: 10.5334/JORS.244
Assistive signals for deep neural network classifiers
Publisher: IEEE
Date: 06-2021
Empirical Autopsy of Deep Video Captioning Encoder-Decoder Architecture
Publisher: Elsevier BV
Date: 03-2021
Redcoat: A Collaborative Annotation Tool for Hierarchical Entity Typing
Publisher: Association for Computational Linguistics
Date: 2019
DOI: 10.18653/V1/D19-3033
E2EET
Publisher: Springer Science and Business Media LLC
Date: 30-11-2022
Detection and interpretation of anomalous water use for non-residential customers
Publisher: Elsevier BV
Date: 02-2018
Representation and recognition of situations in sensor networks
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2010
Effective monotone knowledge integration in kernel support vector machines
Publisher: Springer International Publishing
Date: 2016
Applying cognitive computing to maintainer-collected data
Publisher: No publisher found
Date: 2017
NovelPerspective: Identifying Point of View Characters
Publisher: Association for Computational Linguistics
Date: 2018
DOI: 10.18653/V1/P18-4002
Discovering routine behaviours in smart water meter data
Publisher: IEEE
Date: 04-2015
An Investigation of Recursive Auto-associative Memory in Sentiment Detection
Publisher: Springer Berlin Heidelberg
Date: 2011
Vision to Language
Publisher: Springer International Publishing
Date: 2020
How Preprocessing Affects Unsupervised Keyphrase Extraction
Publisher: Springer Berlin Heidelberg
Date: 2014
Refined Distractor Generation with LSA and Stylometry for Automated Multiple Choice Question Generation
Publisher: Springer Berlin Heidelberg
Date: 2012
Adversarial Attacks and Defense on Deep Learning Classification Models using YCbCr Color Images
Publisher: IEEE
Date: 18-07-2021
DFraud³: Multi-Component Fraud Detection Free of Cold-Start
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
ScoreGAN: A Fraud Review Detector Based on Regulated GAN With Data Augmentation
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Tracking and modelling information diffusion across interactive online media
Publisher: Inderscience Publishers
Date: 2007
Short-Text Lexical Normalisation on Industrial Log Data
Publisher: IEEE
Date: 11-2018
An interactive web-based toolset for knowledge discovery from short text log data
Publisher: Springer International Publishing
Date: 2017
Using Word Embeddings to Enhance Keyword Identification for Scientific Publications
Publisher: Springer International Publishing
Date: 2015
Natural Language Query for Technical Knowledge Graph Navigation
Publisher: Springer Nature Singapore
Date: 2022
Generating Bags of Words from the Sums of Their Word Embeddings
Publisher: Springer International Publishing
Date: 2018
Enhanced Random Forest Algorithms for Partially Monotone Ordinal Classification
Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
Date: 17-07-2019
DOI: 10.1609/AAAI.V33I01.33013224
Abstract: One of the factors hindering the use of classification models in decision making is that their predictions may contradict expectations. In domains such as finance and medicine, the ability to include knowledge of monotone (nondecreasing) relationships is sought after to increase accuracy and user satisfaction. As one of the most successful classifiers, attempts have been made to do so for Random Forest. Ideally a solution would (a) maximise accuracy (b) have low complexity and scale well (c) guarantee global monotonicity and (d) cater for multi-class. This paper first reviews the state-of-theart from both the literature and statistical libraries, and identifies opportunities for improvement. A new rule-based method is then proposed, with a maximal accuracy variant and a faster approximate variant. Simulated and real datasets are then used to perform the most comprehensive ordinal classification benchmarking in the monotone forest literature. The proposed approaches are shown to reduce the bias induced by monotonisation and thereby improve accuracy.
A genetic algorithm enabled ensemble for unsupervised medical term extraction from clinical letters
Publisher: Springer Science and Business Media LLC
Date: 12-2015
Leveraging Linguistically-aware Object Relations and NASNet for Image Captioning
Publisher: IEEE
Date: 25-11-2020
An HMM-SVM-based automatic image annotation approach
Publisher: Springer Berlin Heidelberg
Date: 2011
QuickGraph
Publisher: Association for Computational Linguistics
Date: 2022
Language Model Agnostic Gray-Box Adversarial Attack on Image Captioning
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Modelling Sentence Generation from Sum of Word Embedding Vectors as a Mixed Integer Programming Problem
Publisher: IEEE
Date: 12-2016
Ontology Learning from Text: A look back and into the future
Publisher: Association for Computing Machinery (ACM)
Date: 08-2012
Abstract: Ontologies are often viewed as the answer to the need for interoperable semantics in modern information systems. The explosion of textual information on the Read/Write Web coupled with the increasing demand for ontologies to power the Semantic Web have made (semi-)automatic ontology learning from text a very promising research area. This together with the advanced state in related areas, such as natural language processing, have fueled research into ontology learning over the past decade. This survey looks at how far we have come since the turn of the millennium and discusses the remaining challenges that will define the research directions in this area in the near future.
LCEval: Learned Composite Metric for Caption Evaluation
Publisher: Springer Science and Business Media LLC
Date: 13-08-2019
Spatio-Temporal Dynamics and Semantic Attribute Enriched Visual Encoding for Video Captioning
Publisher: IEEE
Date: 06-2019
Tree-Traversing Ant Algorithm for term clustering based on featureless similarities
Publisher: Springer Science and Business Media LLC
Date: 08-06-2007
Cross-Domain Modality Fusion for Dense Video Captioning
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2022
Seq2KG: An End-to-End Neural Model for Domain Agnostic Knowledge Graph (not Text Graph) Construction from Text.
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 07-2020
DOI: 10.24963/KR.2020/77
Abstract: Knowledge Graph Construction (KGC) from text unlocks information held within unstructured text and is critical to a wide range of downstream applications. General approaches to KGC from text are heavily reliant on the existence of knowledge bases, yet most domains do not even have an external knowledge base readily available. In many situations this results in information loss as a wealth of key information is held within "non-entities". Domain-specific approaches to KGC typically adopt unsupervised pipelines, using carefully crafted linguistic and statistical patterns to extract co-occurred noun phrases as triples, essentially constructing text graphs rather than true knowledge graphs. In this research, for the first time, in the same flavour as Collobert et al.'s seminal work of "Natural language processing (almost) from scratch" in 2011, we propose a Seq2KG model attempting to achieve "Knowledge graph construction (almost) from scratch". An end-to-end Sequence to Knowledge Graph (Seq2KG) neural model jointly learns to generate triples and resolves entity types as a multi-label classification task through deep learning neural networks. In addition, a novel evaluation metric that takes both semantic and structural closeness into account is developed for measuring the performance of triple extraction. We show that our end-to-end Seq2KG model performs on par with a state of the art rule-based system which outperformed other neural models and won the first prize of the first Knowledge Graph Contest in 2019. A new annotation scheme and three high-quality manually annotated datasets are available to help promote this direction of research.
Dense Video Captioning With Early Linguistic Information Fusion
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Related Organisations
Related Funding Activities
Linkage Projects - Grant ID: LP0218928
Start Date: 2002
End Date: 12-2004
Amount: $206,586.00
Funder: Australian Research Council
View Funded ActivityLinkage Projects - Grant ID: LP110100050
Start Date: 11-2011
End Date: 12-2015
Amount: $225,000.00
Funder: Australian Research Council
View Funded ActivityDiscovery Projects - Grant ID: DP150102405
Start Date: 09-2015
End Date: 09-2021
Amount: $509,850.00
Funder: Australian Research Council
View Funded ActivityIndustrial Transformation Training Centres - Grant ID: IC230100035
Start Date: 2023
End Date: 12-2027
Amount: $5,000,000.00
Funder: Australian Research Council
View Funded ActivityIndustrial Transformation Training Centres - Grant ID: IC180100030
Start Date: 02-2019
End Date: 02-2025
Amount: $3,925,357.00
Funder: Australian Research Council
View Funded Activity