ORCID Profile
0000-0003-2237-5017
Current Organisation
Monash University
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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.
Artificial Intelligence and Image Processing | Text Processing | Other Artificial Intelligence | Natural Language Processing | Artificial Intelligence and Image Processing not elsewhere classified | Computational Linguistics | Expert Systems | Intelligent Robotics | Medical and Health Sciences not elsewhere classified | Interdisciplinary Engineering not elsewhere classified | Sensory Processes, Perception And Performance | Linguistic Processes (Incl. Speech Production And Comprehension) | Information Systems | Aged Health Care | Pattern Recognition and Data Mining | Decision Support and Group Support Systems | Decision Making | Computer-Human Interaction | Networking and Communications | Cognitive Science | Psychology | Decision Support And Group Support Systems | Speech Recognition | Pattern Recognition
Computer Software and Services not elsewhere classified | Computer software and services not elsewhere classified | Information processing services | Behavioural and cognitive sciences | Disability and Functional Capacity | Health Related to Ageing | Public Health (excl. Specific Population Health) not elsewhere classified | Information services not elsewhere classified | Information and Communication Services not elsewhere classified | Hearing, vision, speech and their disorders | Computer hardware and electronic equipment not elsewhere classified | Instrumentation not elsewhere classified |
Publisher: Wiley
Date: 02-2001
Publisher: Wiley
Date: 02-1990
Publisher: Springer Science and Business Media LLC
Date: 03-2005
Publisher: Springer International Publishing
Date: 2015
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11553939_126
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: Springer Berlin Heidelberg
Date: 1990
Publisher: Springer Berlin Heidelberg
Date: 1998
DOI: 10.1007/BFB0052318
Publisher: Springer Berlin Heidelberg
Date: 1998
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: Oxford University Press (OUP)
Date: 2014
Publisher: Springer Berlin Heidelberg
Date: 2001
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: IEEE
Date: 06-2013
Publisher: IEEE
Date: 11-2006
DOI: 10.1109/CIMCA.2006.2
Publisher: Association for Computational Linguistics
Date: 2017
DOI: 10.18653/V1/P17-2083
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11527886_5
Publisher: Springer Science and Business Media LLC
Date: 03-06-2015
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: Springer Science and Business Media LLC
Date: 1991
DOI: 10.1007/BF00141048
Publisher: Springer Science and Business Media LLC
Date: 03-2005
Publisher: IEEE
Date: 11-2007
Publisher: IEEE
Date: 12-2008
Publisher: Springer Science and Business Media LLC
Date: 06-07-2013
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: ACM
Date: 13-07-2016
Publisher: Springer Science and Business Media LLC
Date: 2001
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: Springer Science and Business Media LLC
Date: 1993
DOI: 10.1007/BF01099728
Publisher: Wiley
Date: 11-1991
Publisher: Springer Berlin Heidelberg
Date: 2002
Publisher: Springer Science and Business Media LLC
Date: 17-07-2007
Publisher: Springer Science and Business Media LLC
Date: 2001
Publisher: Elsevier BV
Date: 06-2023
Publisher: Elsevier BV
Date: 02-2011
Publisher: ACM
Date: 18-07-2023
Publisher: MIT Press - Journals
Date: 06-2014
DOI: 10.1162/COLI_A_00173
Abstract: Authorship attribution deals with identifying the authors of anonymous texts. Traditionally, research in this field has focused on formal texts, such as essays and novels, but recently more attention has been given to texts generated by on-line users, such as e-mails and blogs. Authorship attribution of such on-line texts is a more challenging task than traditional authorship attribution, because such texts tend to be short, and the number of candidate authors is often larger than in traditional settings. We address this challenge by using topic models to obtain author representations. In addition to exploring novel ways of applying two popular topic models to this task, we test our new model that projects authors and documents to two disjoint topic spaces. Utilizing our model in authorship attribution yields state-of-the-art performance on several data sets, containing either formal texts written by a few authors or informal texts generated by tens to thousands of on-line users. We also present experimental results that demonstrate the applicability of topical author representations to two other problems: inferring the sentiment polarity of texts, and predicting the ratings that users would give to items such as movies.
Publisher: Springer International Publishing
Date: 2018
Publisher: Elsevier BV
Date: 11-2017
Publisher: Springer Berlin Heidelberg
Date: 1990
Publisher: Springer Berlin Heidelberg
Date: 1990
Publisher: Springer Science and Business Media LLC
Date: 09-11-2018
Publisher: Springer Berlin Heidelberg
Date: 2003
Publisher: Springer Berlin Heidelberg
Date: 1998
DOI: 10.1007/BFB0095301
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11527886_27
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: Springer Science and Business Media LLC
Date: 13-02-2007
Publisher: Springer US
Date: 2009
Publisher: Springer Berlin Heidelberg
Date: 2006
DOI: 10.1007/11941439_150
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: Springer Berlin Heidelberg
Date: 2001
Publisher: Elsevier BV
Date: 11-2015
Publisher: MIT Press - Journals
Date: 12-2009
DOI: 10.1162/COLI.2009.35.4.35404
Abstract: This article presents an investigation of corpus-based methods for the automation of help-desk e-mail responses. Specifically, we investigate this problem along two operational dimensions: (1) information-gathering technique, and (2) granularity of the information. We consider two information-gathering techniques (retrieval and prediction) applied to information represented at two levels of granularity (document-level and sentence-level). Document-level methods correspond to the reuse of an existing response e-mail to address new requests. Sentence-level methods correspond to applying extractive multi-document summarization techniques to collate units of information from more than one e-mail. Evaluation of the performance of the different methods shows that in combination they are able to successfully automate the generation of responses for a substantial portion of e-mail requests in our corpus. We also investigate a meta-selection process that learns to choose one method to address a new inquiry e-mail, thus providing a unified response automation solution.
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: Elsevier BV
Date: 12-2016
DOI: 10.1016/J.JBI.2016.10.008
Abstract: Text and data mining play an important role in obtaining insights from Health and Hospital Information Systems. This paper presents a text mining system for detecting admissions marked as positive for several diseases: Lung Cancer, Breast Cancer, Colon Cancer, Secondary Malignant Neoplasm of Respiratory and Digestive Organs, Multiple Myeloma and Malignant Plasma Cell Neoplasms, Pneumonia, and Pulmonary Embolism. We specifically examine the effect of linking multiple data sources on text classification performance. Support Vector Machine classifiers are built for eight data source combinations, and evaluated using the metrics of Precision, Recall and F-Score. Sub-s ling techniques are used to address unbalanced datasets of medical records. We use radiology reports as an initial data source and add other sources, such as pathology reports and patient and hospital admission data, in order to assess the research question regarding the impact of the value of multiple data sources. Statistical significance is measured using the Wilcoxon signed-rank test. A second set of experiments explores aspects of the system in greater depth, focusing on Lung Cancer. We explore the impact of feature selection analyse the learning curve examine the effect of restricting admissions to only those containing reports from all data sources and examine the impact of reducing the sub-s ling. These experiments provide better understanding of how to best apply text classification in the context of imbalanced data of variable completeness. Radiology questions plus patient and hospital admission data contribute valuable information for detecting most of the diseases, significantly improving performance when added to radiology reports alone or to the combination of radiology and pathology reports. Overall, linking data sources significantly improved classification performance for all the diseases examined. However, there is no single approach that suits all scenarios the choice of the most effective combination of data sources depends on the specific disease to be classified.
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: Springer Berlin Heidelberg
Date: 2006
DOI: 10.1007/11941439_59
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: ACM
Date: 06-06-2011
Publisher: Springer Berlin Heidelberg
Date: 1996
Publisher: Springer International Publishing
Date: 2014
Publisher: Springer Berlin Heidelberg
Date: 1996
Publisher: Association for Computational Linguistics
Date: 2017
DOI: 10.18653/V1/D17-1229
Publisher: Springer Berlin Heidelberg
Date: 2008
Location: United States of America
Start Date: 02-2023
End Date: 02-2027
Amount: $650,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 01-2008
End Date: 12-2010
Amount: $315,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 09-2003
End Date: 06-2006
Amount: $195,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 07-2019
End Date: 10-2022
Amount: $408,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 2008
End Date: 12-2010
Amount: $325,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 05-2016
End Date: 12-2019
Amount: $258,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 01-2011
End Date: 04-2015
Amount: $255,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 2003
End Date: 01-2004
Amount: $198,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 12-2008
End Date: 12-2012
Amount: $310,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 12-2020
End Date: 11-2024
Amount: $450,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 03-2011
End Date: 04-2015
Amount: $280,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 06-2012
End Date: 12-2016
Amount: $300,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 2002
End Date: 01-2004
Amount: $206,198.00
Funder: Australian Research Council
View Funded ActivityStart Date: 12-2003
End Date: 12-2004
Amount: $20,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 2003
End Date: 12-2007
Amount: $5,625,165.00
Funder: Australian Research Council
View Funded ActivityStart Date: 12-2004
End Date: 12-2010
Amount: $2,000,000.00
Funder: Australian Research Council
View Funded Activity