Bahadorreza Ofoghi

• BSc, MSc, PhD
• Senior Lecturer at Deakin University - Australia

Goal-oriented conversational systems based on large language models (LLMs) provide the potential capability to gather the necessary requirements for solving tasks or developing solutions. However, in real-world scenarios, non-expert users may respond incorrectly to dialogue questions, which can impede the system’s effectiveness in eliciting accurat...

Linear programming (LP) problems are pervasive in real-life applications. However, despite their apparent simplicity, an untrained user may find it difficult to determine the linear model of their specific problem. We envisage the creation of a goal-oriented conversational agent that will engage in conversation with the user to elicit all informati...

Passage re-ranking in question answering (QA) systems is a method to reorder a set of retrieved passages, related to a given question so that answer-containing passages are ranked higher than non-answer-containing passages. With recent advances in language models, passage ranking has become more effective due to improved natural language understand...

Over the past decades, there has been a surge in the volume of textual data derived from various sources. As the abundance of text carries valuable information and knowledge, summarizing it is extremely desirable. Text summarization is one of the extensively studied and current topics in Natural Language Processing (NLP). Many text summarization te...

Textual Emotion Detection (TED) is a rapidly growing area in Natural Language Processing (NLP) that aims to detect emotions expressed through text. In this paper, we provide a review of the latest research and development in TED as applied in health and medicine. We focus on medical and non-medical data types, use cases, and methods where TED has b...

The Internet of Things (IoT) and the relevant technologies have had a significant impact on smart farming as a major sub-domain within the field of agriculture. Modern technology supports data collection from IoT devices through several farming processes. The extensive amount of collected smart farming data can be utilized for daily decision making...

Question Answering (QA) systems play an important role in today’s human–computer interaction systems. QA performance can be significantly improved using effective answer passage retrieval and ranking techniques. Our focus in this paper is on both non machine learning-based and deep learning-based passage retrieval and ranking systems for QA to leve...

Question Answering (QA) systems play an important role in decision support systems. Deep neural network-based passage rankers have recently been developed to more effectively rank likely answer-containing passages for QA purposes. These rankers utilize distributed word or sentence embeddings. Such distributed representations mostly carry semantic r...

Question Answering (QA) systems play an important role in decision support systems. Deep neural network-based passage rankers have recently been developed to more effectively rank likely answer-containing passages for QA purposes. These rankers utilize distributed word or sentence embeddings. Such distributed representations mostly carry semantic r...

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-minde...

Objectives. There is little understanding of the optimal technical performance characteristics associated with winning matches in netball and how these might vary between competition levels and between each quarter of the game. This study aims to identify and compare team technical performance characteristics between elite domestic and internationa...

Mathematicians formulate complex mathematical models based on user requirements to solve a diverse range of problems in different domains. These models are, in most cases, represented through several mathematical equations and constraints. This modelling task comprises several time-intensive processes that require both mathematical expertise and (p...

With the development of internet technologies, social media and mobile devices, short texts have become an increasingly popular medium among users to communicate with friends, search information and review products. Measuring the similarity between short texts is a fundamental task due to its importance in many applications, such as text retrieval,...

With the rapid development of social networks, short texts have become a prevalent form of social communications on the Internet. Measuring the similarity between short texts is a fundamental task to many applications, such as social network text querying, short text clustering and geographical event detection for smart city. However, short texts i...

This article explores the use of data mining and textual analysis to decipher dispute characteristics, with the goal of developing process referral indicia or 'dispute resolution triage ’for disputants. It seeks to better understand the characteristics o f disputes that eventually result in judicial decisions by examining and exploring available ca...

This paper describes the application and analysis of a previously developed textual emotion classification system (READ-BioMed-EC) on a different data set in the same language with different textual properties. The classifier makes use of a number of lexicon-based and text-based features. The data set originally used to develop this classifier cons...

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 organi...

Syndromic Surveillance has been performed using machine learning and other statistical methods to detect disease outbreaks. These methods are largely dependent on the availability of historical data to train the machine learning-based surveillance system. However, relevant training data may differ from region to region due to geographical and seaso...

Emergency Department Chief Complaints have been used to detect the size and the spread of disease outbreaks in the past. Chief complaints are readily available in digital formats and provide a good data source for syndromic surveillance. This paper reports our findings on the identification of the distribution of a few syndromes over time using the...

Online social media microblogs may be a valuable resource for timely identification of critical ad hoc health-related incidents or serious epidemic outbreaks. In this paper, we explore emotion classification of Twitter microblogs related to localized public health threats, and study whether the public mood can be effectively utilized in early disco...

This paper describes the READ-BioMed team’s participation in the Social Media Mining Shared Task of the Pacific Symposium on Biocomputing 2016. The task had a focus on Adverse Drug Reaction classification of Twitter microblogs. Our READ-BioMed Social media Surveillance system (READ-BioMed-SS) implemented a few lexical normalization processes and em...

Online social media microblogs may be a valuable resource for timely identification of critical ad hoc health-related incidents or serious epidemic outbreaks. In this paper, we explore emotion classification of Twitter microblogs related to localized public health threats, and study whether the public mood can be effectively utilized in early disco...

The development of new methods, devices and apps for self-monitoring have enabled the extension of the application of these approaches for consumer health and research purposes. The increase in the number and variety of devices has generated a complex scenario where reporting guidelines and data exchange formats will be needed to ensure the quality...

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 a...

Biomedical vocabularies vary in scope, and it is often necessary to utilize multiple vocabularies simultaneously in order to cover the full range of concepts relevant to a given biomedical application. However, as the number and size of these resources grow both redundancy (i.e., different vocabularies containing similar terms) and inconsistency (i...

Full text (open access) at: https://www.jsc-journal.com/index.php/JSC/article/view/55/105. The track cycling Omnium is a multi-event competition that has recently been expanded to include the Elimination Race (ER), which presents a unique set of physical and tactical demands. The purpose of this research was to characterise the performance attribut...

Sophisticated data analytical methods such as data mining, where the focus is upon exploration and developing new insights, are becoming increasingly useful tools in analysing elite sports performance data and supporting decision making that is crucial to gaining success. In this article, we investigate the different data mining demands of elite sp...

This article describes the implementation of machine learning techniques that assist cycling experts in the crucial decision-making processes for athlete selection and strategic planning in the track cycling omnium. The omnium is a multi-event competition that was included in the Olympic Games for the first time in 2012. Presently, selectors and cy...

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...

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...

Availability of small amount of data is a major barrier that challenges data mining methods in some applications. To overcome the low accuracy problem, due to system training, we propose a novel hybrid and deterministic approach and test it on rowing championship data. Rowing is a world championship and Olympic sport which requires strategic planni...

In this work, we focused on understanding the required performance levels throughout each section of rowing races to finish in certain positions. We conducted our analysis in terms of each 500-m sector of those races for which historical performance data existed. We considered the ranking and time taken in each sector by a given boat as two importa...

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. O...

This paper presents work on using Machine Learning approaches for predicting performance patterns of medalists in Track Cycling Omnium championships. The omnium is a newly introduced track cycling competition to be included in the London 2012 Olympic Games. It involves six individual events and, therefore, requires strategic planning for riders and...

This paper presents work on using Machine Learning approaches for predicting performance patterns of medalists in Track Cycling Omnium championships. The omnium is a newly introduced track cycling competition to be included in the London 2012 Olympic Games. It involves six individual events and, therefore, requires strategic planning for riders and...

The diagnosis of many sleep disorders is a labor 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 sign...

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...

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-phishin...

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 answer-processing approa...

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 estimatin...

This paper describes a novel approach to profiling phishing emails based on the combination of multi-ple independent clusterings of the email documents. Each clustering is motivated by a natural representa-tion of the emails. A data set of 2048 phishing emails provided by a major Australian financial institution was pre-processed to extract feature...

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 whi...

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 an...

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 p...

Question Answering systems, as an extreme of the Information Retrieval field, could save lots of time and effort in satisfying a specific information need. In this regard, there are still many challenges to be resolved by current state-of-the-art systems as they cope with free texts. We propose a new hybrid question answering schema capable of answ...

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...

In this paper, we will propose a system to extract text documents from web and categorize them for the domain of Telecommunications for further use in TeLQAS, which is an ontology-based natural language question answering system for the domain of Telecommunication Technologies. While, in an online process, TeLQAS accepts the users' questions in Eng...

This paper reports on the implementation and evaluation of a knowledge-based domain-specific question answering system called TeLQAS. This system employs a reasoning engine built based on an extended version of Human Plausible Reasoning theory. The knowledge base of the system has been filled manually with logical statements about Fiber Optics. An...

This paper examines clustering in datasets where each record is presented as a curve (trend). Our clustering approach is based on nonsmooth and non- convex optimisation, namely the clustering problems have been formulated as mathematical programming problems and several optimisation methods from GANSO optimisation library have been applied to solve...

This work explores the application of Human Plausible Reasoning (HPR) in building knowledge-based question answering systems. TeLQAS is an ontology-based domain-specific QA system for Telecommunications. We describe an attempt to use HPR as defined by Collins and Michalski as the core reasoning component of that system. The basic idea is to infer p...

This paper describes our Recognizing Tex-tual Entailment (RTE) system developed at University of Ballarat, Australia for par-ticipation in the Text Analysis Conference RTE 2010 competition. This year, we participated in the Main task and used a machine learning approach for learn-ing textual entailment relationships using parse-free lexical semanti...