Opinion search in web logs
- Authors: Osman, Deanna , Yearwood, John
- Date: 2007
- Type: Text , Conference paper
- Relation: Paper presented at Eighteenth Australasian Database Conference, ADC 2007, Ballarat, Victoria : 29th January-2nd February 2007 p. 133-139
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- Description: Web logs(blogs) are a fast growing forum for people of all ages to express their feelings and opinions on topics of interest. The entries are often written in informal language without the structure found in newswire or published articles. One blog entry may contain many topics, these topics may express an opinion or a fact on a particular topic. This research is in contrast to work on opinion detection which has been carried out on more formally authored texts and on segments that are either whole documents or sentences. Whole web logs are divided into topics using a simple text segmentation approach. Similarity scores are used to distinguish where topic changers occur. The results are compared to human-evaluated topic changes and the most accurate algorithm is used in the remainder of the research. Words within each topic-block are allocated weightings depending on their opinion-bearing strength. Two approaches of using these weights, the sum and the maximum, are used to determine whether the topic-block is opinion-bearing or non-opinion-bearing. The opinion-bearing topic-blocks are rated by human evaluators as either opinion-bearing or non-opinion-bearing with precision of 67% for approach A and 70% for approach B. These results are compared with two approaches on published text to identify the difference between web logs and published articles.
- Description: 2003004895
The study of drug-reaction relationships using global optimization techniques
- Authors: Mammadov, Musa , Rubinov, Alex , Yearwood, John
- Date: 2007
- Type: Text , Journal article
- Relation: Optimization Methods and Software Vol. 22, no. 1 (2007), p. 99-126
- Full Text: false
- Reviewed:
- Description: In this paper we develop an optimization approach for the study of adverse drug reaction (ADR) problems. This approach is based on drug-reaction relationships represented in the form of a vector of weights, which can be defined as a solution to some global optimization problem. Although it can be used for solving many ADR problems, we concentrate on two of them here: the accurate identification of drugs that are responsible for reactions that have occurred, and drug-drug interactions. Based on drug-reaction relationships, we formulate these problems as an optimization problem. The approach is applied to cardiovascularn-type reactions from the Australian Adverse Drug Reaction Advisory Committee (ADRAC) database. Software based on this approach has been developed and could have beneficial use in prescribing.
- Description: C1
- Description: 2003002217
Two-step comprehensive open domain text annotation with frame semantics
- Authors: Ofoghi, Bahadorreza , Yearwood, John , Ma, Liping
- Date: 2007
- Type: Text , Conference paper
- Relation: Paper presented at Australasian Language Technology Workshop 2007, Melbourne Zoo, Melbourne, Victoria : 10th-11th December 2007 p. 83-91
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- Description: With shallow semantic parsing tasks receiving more attention in many natural language applications, there is a need for labelled corpora for learning the specific tags under consideration. In this paper, we discuss a two-step semantic class and semantic role assignment based on the FrameNet elements over a subset of the AQUAINT collection with a reasonable coverage over the semantic frames in FrameNet. The quality of the annotation task is examined through inter-annotator agreement. The methodology described in this work for measuring inter-annotator agreement can be adapted for similar tasks. Some central aspects of the task are also detailed in this paper.
- Description: 2003005522
Using corpus analysis to inform research into opinion detection in blogs
- Authors: Osman, Deanna , Yearwood, John , Vamplew, Peter
- Date: 2007
- Type: Text , Conference paper
- Relation: Paper presented at Sixth Australasian Data Mining Conference, AusDM 2007, Gold Coast, Queensland, Victoria : 3rd-4th December 2007 p. 65-75
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- Description: Opinion detection research relies on labeled documents for training data, either by assumptions based on the document's origin or by using human assessors to categorise the documents. In recent years, blogs have become a source for opinion identification research (TREC Blog06). This study analyses the part-of-speech proportion and the words used within various corpora, determining key differences and similarities useful when preparing for opinion identification research. The resulting comparisons between the characteristics of the various corpora is detailed and discussed. In particular, opinion bearing and non opinion Blog06 documents were found to display a high level of similarity, indicating that blog documents assessed at the document level cannot be used as training data in opinion identification research.
- Description: 2003004892
Using links to aid web classification
- Authors: Xie, Wei , Mammadov, Musa , Yearwood, John
- Date: 2007
- Type: Text , Conference paper
- Relation: Paper presented at 6th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2007, Melbourne, Victoria : 11th-13th July 2007 p. 981-986
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- Description: In this paper, we will present a new approach of using link information to improve the accuracy and efficiency of web classification. However, different from others, we only use the mappings between linked documents and their own class or classes. In this case, we only need to add a few features called linked-class features into the datasets. We apply SVM and BoosTexter for classification. We show that the classification accuracy can be improved based on mixtures of ordinary word features and out-linked-class features. We analyze and discuss the reason of this improvement.
- Description: 2003005438
Visual tools for analysing evolution, emergence, and error in data streams
- Authors: Hart, Sol , Yearwood, John , Bagirov, Adil
- Date: 2007
- Type: Text , Conference paper
- Relation: Paper presented at 6th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2007, Melbourne, Victoria : 11th-13th July 2007 p. 987-992
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- Description: The relatively new field of stream mining has necessitated the development of robust drift-aware algorithms that provide accurate, real time, data handling capabilities. Tools are needed to assess and diagnose important trends and investigate drift evolution parameters. In this paper, we present two new and novel visualisation techniques, Pixie and Luna graphs, which incorporate salient group statistics coupled with intuitive visual representations of multidimensional groupings over time. Through the novel representations presented here, spatial interactions between temporal divisions can be diagnosed and overall distribution patterns identified. It provides a means of evaluating in non-constrained capacity, commonly constrained evolutionary problems.
- Description: 2003005432
A hybrid neural learning algorithm using evolutionary learning and derivative free local search method
- Authors: Ghosh, Ranadhir , Yearwood, John , Ghosh, Moumita , Bagirov, Adil
- Date: 2006
- Type: Text , Journal article
- Relation: International Journal of Neural Systems Vol. 16, no. 3 (2006), p. 201-213
- Full Text: false
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- Description: In this paper we investigate a hybrid model based on the Discrete Gradient method and an evolutionary strategy for determining the weights in a feed forward artificial neural network. Also we discuss different variants for hybrid models using the Discrete Gradient method and an evolutionary strategy for determining the weights in a feed forward artificial neural network. The Discrete Gradient method has the advantage of being able to jump over many local minima and find very deep local minima. However, earlier research has shown that a good starting point for the discrete gradient method can improve the quality of the solution point. Evolutionary algorithms are best suited for global optimisation problems. Nevertheless they are cursed with longer training times and often unsuitable for real world application. For optimisation problems such as weight optimisation for ANNs in real world applications the dimensions are large and time complexity is critical. Hence the idea of a hybrid model can be a suitable option. In this paper we propose different fusion strategies for hybrid models combining the evolutionary strategy with the discrete gradient method to obtain an optimal solution much quicker. Three different fusion strategies are discussed: a linear hybrid model, an iterative hybrid model and a restricted local search hybrid model. Comparative results on a range of standard datasets are provided for different fusion hybrid models. © World Scientific Publishing Company.
- Description: C1
- Description: 2003001712
A hybrid question answering schema using encapsulated semantics in lexical resources
- Authors: Ofoghi, Bahadorreza , Yearwood, John , Ghosh, Ranadhir
- Date: 2006
- Type: Text , Conference paper
- Relation: Paper presented at Artificial Intelligence, AI 2006: Advances in Artificial Intelligence, Hobart : 4th December, 2006 p. 1276-1280
- Full Text: false
- Reviewed:
- Description: E1
- Description: 2003001531
A new nonsmooth optimization algorithm for minimum sum-of-squares clustering problems
- Authors: Bagirov, Adil , Yearwood, John
- Date: 2006
- Type: Text , Journal article
- Relation: European Journal of Operational Research Vol. 170, no. 2 (2006), p. 578-596
- Full Text: false
- Reviewed:
- Description: The minimum sum-of-squares clustering problem is formulated as a problem of nonsmooth, nonconvex optimization, and an algorithm for solving the former problem based on nonsmooth optimization techniques is developed. The issue of applying this algorithm to large data sets is discussed. Results of numerical experiments have been presented which demonstrate the effectiveness of the proposed algorithm. © 2004 Elsevier B.V. All rights reserved.
- Description: C1
- Description: 2003001520
A semantic approach to boost passage retrieval effectiveness for question answering
- Authors: Ofoghi, Bahadorreza , Yearwood, John , Ghosh, Ranadhir
- Date: 2006
- Type: Text , Conference paper
- Relation: Paper presented at Computer Science 2006 Twenty-Ninth Australian Computer Science Conference, Hobart : 16th January, 2006 p. 95-101
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- Description: In the current state of the rapid growth of information resources and the huge number of requests submitted by users to existing information retrieval systems; recently, Question Answering systems have attracted more attention to meet information needs providing users with more precise and focused retrieval units. As one of the most challenging and important processes of such systems is to retrieve the best related text excerpts with regard to the questions, we propose a novel approach to exploit not only the syntax of the natural language of the questions and texts, but also the semantics relayed beneath them via a semantic question rewriting and passage retrieval task. The semantic structure used to address the surface mismatch of the semantically related passages and queries is FrameNet which is a lexical resource for English constituted based on frame semantics. We have run our proposed approach on a subset of the TREC 2004 factoid questions to retrieve passages containing correct answers from the AQUAINT collection and we have obtained promising results.
- Description: E1
- Description: 2003001803
A semantic method to information extraction for decision support systems
- Authors: Ofoghi, Bahadorreza , Yearwood, John , Ghosh, Ranadhir
- Date: 2006
- Type: Text , Conference proceedings
- Full Text: false
- Description: In this paper, we describe a novel schema for a more semantic text mining process which results in more comprehensive decision making activity by decision support systems via providing more effective and accurate textual information. The utility of two semantic lexical resources; Frame Net and Word Net, in extracting required text snippets from unstructured free texts yields a better and more accurate information extraction process to deliver more precise information either to a DSS or to a decision maker. We explain how the usage of these lexical resources could elevate a focused text mining process which could be applied to an information provider system in a decision support paradigm. The preliminary results obtained after a starter experiment show that the hybrid information extraction schema performs well on some semantic failure situations.
- Description: 2003010644
A Tool for Assisting Group Decision-Making for Consensus Outcomes in Organizations
- Authors: Afshar, Faye , Yearwood, John , Stranieri, Andrew
- Date: 2006
- Type: Text , Book chapter
- Relation: E-Supply Chain Technologies and Management p. 316-343
- Full Text: false
- Reviewed:
A variable initialization approach to the EM algorithm for better estimation of the parameters of hidden Markov Model based acoustic modeling of speech signals
- Authors: Huda, Shamsul , Ghosh, Ranadhir , Yearwood, John
- Date: 2006
- Type: Text , Conference paper
- Relation: Paper presented at Artificial Intelligence, Advances in Data Mining, Applications in Medicine, Web Mining, Marketing, Image and Signal Mining Conference 2006, Leipzig, Germany : 14th July, 2006 p. 416-430
- Full Text: false
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- Description: The traditional method for estimation of the parameters of Hidden Markov Model (HMM) based acoustic modeling of speech uses the Expectation-Maximization (EM) algorithm. The EM algorithm is sensitive to initial values of HMM parameters and is likely to terminate at a local maximum of likelihood function resulting in non-optimized estimation for HMM and lower recognition accuracy. In this paper, to obtain better estimation for HMM and higher recognition accuracy, several candidate HMMs are created by applying EM on multiple initial models. The best HMM is chosen from the candidate HMMs which has highest value for likelihood function. Initial models are created by varying maximum frame number in the segmentation step of HMM initialization process. A binary search is applied while creating the initial models. The proposed method has been tested on TIMIT database. Experimental results show that our approach obtains improved values for likelihood function and improved recognition accuracy.
- Description: E1
- Description: 2003001542
An interaction framework for scenario-based three dimensional environments
- Authors: Macfadyen, Alyx , Stranieri, Andrew , Yearwood, John
- Date: 2006
- Type: Text , Conference paper
- Relation: Paper presented at IE 2006, the 3rd Australasian Conference on Interactive Entertainment, Perth : 4th December, 2006
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- Description: Although popular and engaging, three dimensional environments are rarely deployed to depict strong narratives involving complex characters engaged in reasoning. The design of three dimensional environments rich in narrative and character depth can be facilitated with a detailed representation of interactions between characters. However, the representation of interaction in current 3D development environments such as game engines is quite basic. This work advances a scheme for representing interactions that integrates a representation of semantics from linguistics called FrameNet with conceptualizations of drama and narrative by Georges Polti and Joseph Campbell. The resulting interaction frame facilitates the design of 3D environments by providing designers rich, yet standard elements that include spatial and temporal data, with which to represent complex interactions in 3D environments. This has application for the authoring of dynamically generated interactive narrative environments.
- Description: E1
- Description: 2003001839
The generic/actual argument model of practical reasoning
- Authors: Yearwood, John , Stranieri, Andrew
- Date: 2006
- Type: Text , Journal article
- Relation: Decision Support Systems Vol. 41, no. 2 (2006), p. 358-379
- Full Text: false
- Reviewed:
- Description: In this paper, we present a model of reasoning called the generic/actual argument model (GAAM). Reasoning within a discursive community can be represented with this model so that participant claims can be accommodated without recourse to combative metaphors such as attack or defeat. The model facilitates the comprehension of complex reasoning for humans as well as being a computational representation for machine modelling of reasoning. As such, the model naturally integrates machine inferences with human. The model has been the basis for the development of practical systems to support reasoning and deliberation in areas of law and organizational decision making. Here, we present a formal description of the model and identify some of its characteristics. © 2004 Elsevier B.V. All rights reserved.
- Description: C1
- Description: 2003001594
Using association and overlapping time window approach to detect drug reaction signals
- Authors: Ivkovic, Sasha , Saunders, Gary , Ghosh, Ranadhir , Yearwood, John
- Date: 2006
- Type: Text , Conference paper
- Relation: Paper presented at CIMCA 2005 International Conference on Computational Intelligence for Modelling Control & Automation jointly with IAWTIC 2005 International Conference on Intelligent Agents, Web Technologies & Internet Commerce, Vienna, Austria : 28th November, 2005 p. 1045-1053
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- Reviewed:
- Description: The problem with detecting adverse drug reactions (ADRs) from drugs is that they may not be obvious until long after they are widely prescribed. Part of the problem is these events are rare. This work describes an approach to signal detection of ADRs based on association rules (AR) in Australian drug safety data. This work was carried out using the Australian Adverse Drug Reactions Advisory Committee (ADRAC) database, which contains a hundred and thirty seven thousand records collected in 1972-2001 period. Many signal detection methods have been developed for drug safety data, most of which use a classical statistical approach. Some of these stratify the data using an ontology for reactions, but the application of drug ontologies to ADR signal detection methods has not been reported. We propose a novel approach for detecting various signal levels by using an overlapped windowing approach. The overlapping windows help to detect smooth transition of signal. We use association rules for measuring significant change over time for different hierarchical levels of drugs (using the Anatomical-Therapeutic-Chemical (ATC) system of drug classification ontology) and their reactions based on the System Organ Classes (SOC) ontology. Using association rules and their strength for different levels in the drug and reaction hierarchy, helps in the detection of signals at particular levels in higher order using a bottom up approach. The results of a preliminary investigation of ADRAC data using our method demonstrate that this approach could produce a powerful and robust ADR signal detection method.
- Description: E1
- Description: 2003001838
A CAD system using clustering and novel feature extraction technique
- Authors: Ghosh, Ranadhir , Ghosh, Moumita , Yearwood, John
- Date: 2005
- Type: Text , Conference paper
- Relation: Paper presented at CISTM 2005, Gurgaon, India : 24th - 26th July, 2005
- Full Text: false
- Reviewed:
- Description: Many previous efforts have utilized many different approaches for recognition in breast cancer detection using various ANN classifier-modelling techniques. Most of the previous work was concentred mostly on the classification of the damaged areas with the help of doctor’s suggestion. Doctors use to mark the suspicious areas area in the mammogram and the classifier only extract those marked areas and tries to classify it. An intelligent automatic diagnosis system can be very helpful for radiologist in diagnosing Breast cancer. In this research we are applying a local search gradient free clustering algorithm to find out the suspicious / damaged area. We compare our results with the doctor’s marking. Also it has been observed that, beyond a certain point, the inclusion of additional features leads to a worse rather than better performance. Moreover, the choice of features to represent the patterns affects several aspects of pattern recognition problems such as accuracy, required learning time and a necessary number of samples. A common problem with the multi-category feature classification is the conflict between the categories. None of the feasible solutions allow simultaneous optimal solution for all categories. In order to find an optimal solution the search space can be divided based on an individual category in each sub region and finally merging them through decision spport system. Combining the feature selection with the classifier has been a major challenge for the researchers. A similar technique employed in both the levels often worsens their performance. Some preliminary studies has revealed that while using traditional canonical GA has been a good choice for feature selection modules, however under perform for the classifier level module. An evolutionary based algorithm for the classifier level provides a much better solution for this purpose. In this paper we propose a hybrid canonical based feature extraction technique with a combination of evolutionary algorithm based classifier using a feed forward MLP model.
- Description: E1
- Description: 2003001369
A formal description of ontology change in OWL
- Authors: Avery, John , Yearwood, John
- Date: 2005
- Type: Text , Conference paper
- Relation: Paper presented at the Third International Conference on Information Technology and Applications, ICITA 2005, Sydney : 4th - 7th July, 2005
- Full Text:
- Reviewed:
- Description: There are three main activities involved in managing ontology change. Firstly we need to identify changes, secondly describe these identified changes, and finally describe and handle the ramifications of the changes. In previous work we have presented a language (DOWL) for describing ontology change and in this paper we demonstrate how changes described in this language can be represented in the RDF abstract syntax which enables us to describe the ramifications of a change in a formal manner. This formalism can provide the basis for an automated ontology change management system.
- Description: E1
- Description: 2003001448
A fully automated breast cancer recognition system using discrete-gradient based clustering and multi category feature selection
- Authors: Ghosh, Ranadhir , Ghosh, Moumita , Yearwood, John
- Date: 2005
- Type: Text , Journal article
- Relation: Journal of Advanced Computational Intelligence and Intelligent Informatics Vol. 9, no. 3 (2005), p. 244-256
- Full Text: false
- Reviewed:
- Description: Advances in machine intelligence have provided a whole new window of opportunities in medical research. Building a fully automated computer aided diagnostic system for digital mammograms is just one of them. Given some success with semi-automated systems earlier, a fully automated CAD system is just another step forward. A proper combination of a feature selection model and a classifier for those areas of a mammogram marked by radiologists has been very successful. However a fully automated system with only two modules is a time consuming process as the suspicious areas in a mammogram can be quite small when compared to the whole image. Thus an additional clustering process can help in reducing the time complexity of the overall process. In this paper we propose a fast clustering process to identify suspicious areas. Another novelty of this paper is a multi-category feature selection approach. The choice of features to represent the patterns affects several aspects of pattern recognition problems such as accuracy, required learning time and the required number of samples. In this paper we propose a hybrid canonical based feature extraction technique as a combination of an evolutionary algorithm based classifier with a feed forward MLP model.
- Description: C1
- Description: 2003001358
A Hybrid algorithm for estimation of the parameters of Hidden Markov Model based acoustic modeling of speech signals using constraint-based genetic algorithm and expectation maximization
- Authors: Ghosh, Ranadhir , Huda, Shamsul , Yearwood, John
- Date: 2005
- Type: Text , Conference paper
- Relation: Paper presented at the Workshop in Learning Algorithms for Pattern Recognition, in conjunction with the 18th Australian Joint Conference on Artificial Intelligence, Sydney : 5th December, 2005
- Full Text: false
- Reviewed:
- Description: E1
- Description: 2003001368