Applying clustering and ensemble clustering approaches to phishing profiling
- Authors: Webb, Dean , Yearwood, John , Vamplew, Peter , Ma, Liping , Ofoghi, Bahadorreza , Kelarev, Andrei
- Date: 2009
- Type: Text , Conference paper
- Relation: Paper presented at Eighth Australasian Data Mining Conference, AusDM 2009, University of Melbourne, Melbourne, Victoria : 1st–4th December 2009
- Full Text:
- Description: 2003007911
The impact of semantic class identification and semantic role labeling on natural language answer extraction
- Authors: Ofoghi, Bahadorreza , Yearwood, John , Ma, Liping
- Date: 2008
- Type: Text , Conference paper
- Relation: Paper presented at 30th European Conference on IR Research, ECIR 2008, Glasgow, UK : 30th March - 3rd April 2008 p. 430-437
- Full Text: false
- Description: In satisfying an information need by a Question Answering (QA) system, there are text understanding approaches which can enhance the performance of final answer extraction. Exploiting the FrameNet lexical resource in this process inspires analysis of the levels of semantic representation in the automated practice where the task of semantic class and role labeling takes place. In this paper, we analyze the impact of different levels of semantic parsing on answer extraction with respect to the individual sub-tasks of frame evocation and frame element assignment.
- Description: 2003006587
The impact of frame semantic annotation levels, frame-alignment techniques, and fusion methods on factoid answer processing
- Authors: Ofoghi, Bahadorreza , Yearwood, John , Liping, Ma
- Date: 2009
- Type: Text , Journal article
- Relation: Journal of the American Society for Information Science and Technology Vol. 60, no. 2 (2009), p. 247-263
- Full Text: false
- Reviewed:
- Description: The impact of frame semantic enrichment of texts on the task of factoid question answering (QA) is studied in this paper. In particular, we consider different techniques for answer processing with frame semantics: the level of semantic class identification and role assignment to texts, and the fusion of frame semantic-based answerprocessing approaches with other methods used in the Text REtrieval Conference (TREC). The impact of each of these aspects on the overall performance of a QA system is analyzed in this paper. The TREC 2004 and TREC 2006 factoid question sets were used for the experiments. These demonstrate that the exploitation of encapsulated frame semantics in FrameNet in a shallow semantic parsing process can enhance answer-processing performance in factoid QA systems. This improvement is dependent on the level of semantic annotation, the frame semantic alignment method, and the method of fusing frame semantic-based answer-processing models with other existing models. A more comprehensively annotated environment with all different part-of-speech target predicates provides a higher chance of correct factoid answer retrieval where semantic alignment is based on both semantic classes and a relaxed set of semantic roles for answer span identification. Our experiments on fusion techniques of frame semantic-based and entity-based answer-processing models show that merging answer lists with respect to their scores and redundancy by exploiting a fusion function leads to a more effective overall factoid QA system compared to the use of individual models.
Detecting phishing emails using hybrid features
- Authors: Ma, Liping , Ofoghi, Bahadorreza , Watters, Paul , Brown, Simon
- Date: 2009
- Type: Text , Conference paper
- Relation: Paper presented at 2009 Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC '09, Brisbane, Queensland : 7th-9th July 2009 p. 493-497
- Full Text:
- Description: Phishing emails have been used widely in fraud of financial organizations and customers. Phishing email detection has drawn a lot attention for many researchers and malicious detection devices are installed in email servers. However, phishing has become more and more complicated and sophisticated and attack can bypass the filter set by anti-phishing techniques. In this paper, we present a method to build a robust classifier to detect phishing emails using hybrid features and to select features using information gain. We experiment on 10 cross-validations to build an initial classifier which performs well. The experiment also analyses the quality of each feature using information gain and best feature set is selected after a recursive learning process. Experimental result shows the selected features perform as well as the original features. Finally, we test five machine learning algorithms and compare the performance of each. The result shows that decision tree builds the best classifier.
- Description: 2003007857
Enhancing factoid question answering using frame semantic-based approaches
- Authors: Ofoghi, Bahadorreza
- Date: 2009
- Type: Text , Thesis , PhD
- Full Text:
- Description: FrameNet is used to enhance the performance of semantic QA systems. FrameNet is a linguistic resource that encapsulates Frame Semantics and provides scenario-based generalizations over lexical items that share similar semantic backgrounds.
- Description: Doctor of Philosophy
Learning parse-free event-based features for textual entailment recognition
- Authors: Ofoghi, Bahadorreza , Yearwood, John
- Date: 2010
- Type: Text , Conference paper
- Relation: Paper presented at 23rd Australasian Joint Conference on Artificial Intelligence, AI 2010 Vol. 6464 LNAI, p. 184-193
- Full Text: false
- Reviewed:
- Description: We propose new parse-free event-based features to be used in conjunction with lexical, syntactic, and semantic features of texts and hypotheses for Machine Learning-based Recognizing Textual Entailment. Our new similarity features are extracted without using shallow semantic parsers, but still lexical and compositional semantics are not left out. Our experimental results demonstrate that these features can improve the effectiveness of the identification of entailment and no-entailment relationships. © 2010 Springer-Verlag.
A within-frame ontological extension on FrameNet : Application in predicate chain analysis and question answering
- Authors: Ofoghi, Bahadorreza , Yearwood, John , Ghosh, Ranadhir
- Date: 2007
- Type: Text , Conference paper
- Relation: Paper presented at 20th Australian Joint Conference on Artificial Intelligence, AI 2007: Advances in Artificial Intelligence, Gold Coast, Queensland : 2nd-6th December 2007 p. 404-414
- Full Text: false
- Description: An ontological extension on the frames in FrameNet is presented in this paper. The general conceptual relations between frame elements, in conjunction with existing characteristics of this lexical resource, suggest more sophisticated semantic analysis of lexical chains (e.g. predicate chains) exploited in many text understanding applications. In particular, we have investigated its benefit for meaning-aware question answering when combined with an inference strategy. The proposed knowledge representation mechanism on the frame elements of FrameNet has been shown to have an impact on answering natural language questions on the basis of our case analysis.
- Description: 2003005507
FrameNet-based fact-seeking answer processing : A study of semantic alignment techniques and lexical coverage
- Authors: Ofoghi, Bahadorreza , Yearwood, John , Ma, Liping
- Date: 2008
- Type: Text , Journal article
- Relation: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 5360 LNAI, no. (1 December 2008 through 5 December 2008 2008), p. 192-201
- Full Text: false
- Description: In this paper, we consider two aspects which affect the performance of factoid FrameNet-based Question Answering (QA): i) the frame semantic-based answer processing technique based on frame semantic alignment between questions and passages to identify answer candidates and score them, and ii) the lexical coverage of FrameNet over the predicates which represent the main actions in question and passage events. These are studied using a frame semantic-based QA run over the TREC 2004 and TREC 2006 factoid question sets. © 2008 Springer Berlin Heidelberg.
Can shallow semantic class information help answer passage retrieval?
- Authors: Ofoghi, Bahadorreza , Yearwood, John
- Date: 2009
- Type: Text , Conference paper
- Relation: Paper presented at 22nd Australasian Joint Conference, AI 2009: Advances in Artificial Intelligence, Melbourne, Victoria : 1st-4th December 2009 p. 587–596
- Full Text: false
- Description: In this paper, the effect of using semantic class overlap evidence in enhancing the passage retrieval effectiveness of question answering (QA) systems is tested. The semantic class overlap between questions and passages is measured by evoking FrameNet semantic frames using a shallow term-lookup procedure. We use the semantic class overlap evidence in two ways: i) fusing passage scores obtained from a baseline retrieval system with those obtained from the analysis of semantic class overlap (fusion-based approach), and ii) revising the passage scoring function of the baseline system by incorporating semantic class overlap evidence (revision-based approach). Our experiments with the TREC 2004 and 2006 datasets show that the revision-based approach significantly improves the passage retrieval effectiveness of the baseline system.
- Description: 2003007254
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
- Full Text:
- 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
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
- Full Text:
- Reviewed:
- 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 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 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
Novel weighting in single hidden layer feedforward neural networks for data classification
- Authors: Seifollahi, Sattar , Yearwood, John , Ofoghi, Bahadorreza
- Date: 2012
- Type: Text , Journal article
- Relation: Computers and Mathematics with Applications Vol. 64, no. 2 (2012), p. 128-136
- Full Text: false
- Reviewed:
- Description: We propose a binary classifier based on the single hidden layer feedforward neural network (SLFN) using radial basis functions (RBFs) and sigmoid functions in the hidden layer. We use a modified attribute-class correlation measure to determine the weights of attributes in the networks. Moreover, we propose new weights called as influence weights to utilize in the weights connecting the input layer and the hidden layer nodes (hidden weights) of the network with sigmoid hidden nodes. These weights are calculated as the sum of conditional probabilities of attribute values given class labels. Our learning procedure of the networks is based on the extreme learning machines; in which the parameters of the hidden nodes are first calculated and then the weights connecting the hidden nodes and output nodes (output weights) are found. The results of the networks with the proposed weights on some benchmark data sets show improvements over those of the conventional networks. © 2012 Elsevier Ltd. All rights reserved.
From lexical entailment to recognizing textual entailment using linguistic resources
- Authors: Ofoghi, Bahadorreza , Yearwood, John
- Date: 2009
- Type: Text , Conference paper
- Relation: Paper presented at Australasian Language Technology Association Workshop 2009, Sydney, New South Wales : 3rd-4th December 2009 p. 119–123
- Full Text:
- Description: In this paper, we introduce our Recognizing Textual Entailment (RTE) system developed on the basis of Lexical Entailment between two text excerpts, namely the hypothesis and the text. To extract atomic parts of hypotheses and texts, we carry out syntactic parsing on the sentences. We then utilize WordNet and FrameNet lexical resources for estimating lexical coverage of the text on the hypothesis. We report the results of our RTE runs on the Text Analysis Conference RTE datasets. Using a failure analysis process, we also show that the main difficulty of our RTE system relates to the underlying difficulty of syntactic analysis of sentences.
- Description: 2003007910
Automatic sleep stage identification: difficulties and possible solutions
- Authors: Sukhorukova, Nadezda , Stranieri, Andrew , Ofoghi, Bahadorreza , Vamplew, Peter , Saleem, Muhammad Saad , Ma, Liping , Ugon, Adrien , Ugon, Julien , Muecke, Nial , Amiel, Hélène , Philippe, Carole , Bani-Mustafa, Ahmed , Huda, Shamsul , Bertoli, Marcello , Levy, P , Ganascia, J.G
- Date: 2010
- Type: Text , Conference proceedings
- Full Text:
- Description: The diagnosis of many sleep disorders is a labour intensive task that involves the specialised interpretation of numerous signals including brain wave, breath and heart rate captured in overnight polysomnogram sessions. The automation of diagnoses is challenging for data mining algorithms because the data sets are extremely large and noisy, the signals are complex and specialist's analyses vary. This work reports on the adaptation of approaches from four fields; neural networks, mathematical optimisation, financial forecasting and frequency domain analysis to the problem of automatically determing a patient's stage of sleep. Results, though preliminary, are promising and indicate that combined approaches may prove more fruitful than the reliance on a approach.