Relevance feature discovery for text analysis

It is a big challenge to guarantee the quality of discovered relevance features in text documents for describing user preferences because of the large number of terms, patterns, and noise. Most existing popular text mining and classification methods have adopted term-based approaches. However, they have all suffered from the problems of polysemy and synonymy. Over the years, people have often held the hypothesis that pattern-based methods should perform better than term- based ones in describing user preferences, but many experiments do not support this hypothesis. This research presents a promising method, Relevance Feature Discovery (RFD), for solving this challenging issue. It discovers both positive and negative patterns in text documents as high-level features in order to accurately weight low-level features (terms) based on their specificity and their distributions in the high-level features. The thesis also introduces an adaptive model (called ARFD) to enhance the exibility of using RFD in adaptive environment. ARFD automatically updates the system's knowledge based on a sliding window over new incoming feedback documents. It can efficiently decide which incoming documents can bring in new knowledge into the system. Substantial experiments using the proposed models on Reuters Corpus Volume 1 and TREC topics show that the proposed models significantly outperform both the state-of-the-art term-based methods underpinned by Okapi BM25, Rocchio or Support Vector Machine and other pattern-based methods.

Rule-based approach for identifying assertions in clinical free-text data

A rule-based approach for classifying previously identified medical concepts in the clinical free text into an assertion category is presented. There are six different categories of assertions for the task: Present, Absent, Possible, Conditional, Hypothetical and Not associated with the patient. The assertion classification algorithms were largely based on extending the popular NegEx and Context algorithms. In addition, a health based clinical terminology called SNOMED CT and other publicly available dictionaries were used to classify assertions, which did not fit the NegEx/Context model. The data for this task includes discharge summaries from Partners HealthCare and from Beth Israel Deaconess Medical Centre, as well as discharge summaries and progress notes from University of Pittsburgh Medical Centre. The set consists of 349 discharge reports, each with pairs of ground truth concept and assertion files for system development, and 477 reports for evaluation. The system’s performance on the evaluation data set was 0.83, 0.83 and 0.83 for recall, precision and F1-measure, respectively. Although the rule-based system shows promise, further improvements can be made by incorporating machine learning approaches.

An Introduction to Political Communication, 5th Edition

An Introduction to Political Communication introduces students to the complex relationship between politics, the media and democracy in the United Kingdom, United States and other contemporary societies. Brian McNair examines how politicians, trade unions, pressure groups, NGOs and terrorist organisations make use of the media. Individual chapters look at political media and their effects, the work of political advertising, marketing and public relations, and the communicative practices of organizations at all levels, from grass-root campaigning through to governments and international bodies. This fifth edition has been revised and updated to include: • the 2008 US presidential election, and the early years of Barack Obama’s term • the MPs’ expenses scandal in Britain, and the 2010 UK election campaign • the growing role of bloggers and online pundits such as Guido Fawkes in the political agenda setting process • the emergence of social media platforms such as Twitter, YouTube and Facebook, and their destabiising impact on the management of political crises all over the world, including the Iranian pro-reform protests of July 2009 and the Israeli atack on the anti-blockade flotilla of May 2010 • the growing power of Wikileaks and other online information sources to challenge state control of classified information

Text mining and probabilistic language modeling for online review spam detecting

In the era of Web 2.0, huge volumes of consumer reviews are posted to the Internet every day. Manual approaches to detecting and analyzing fake reviews (i.e., spam) are not practical due to the problem of information overload. However, the design and development of automated methods of detecting fake reviews is a challenging research problem. The main reason is that fake reviews are specifically composed to mislead readers, so they may appear the same as legitimate reviews (i.e., ham). As a result, discriminatory features that would enable individual reviews to be classified as spam or ham may not be available. Guided by the design science research methodology, the main contribution of this study is the design and instantiation of novel computational models for detecting fake reviews. In particular, a novel text mining model is developed and integrated into a semantic language model for the detection of untruthful reviews. The models are then evaluated based on a real-world dataset collected from amazon.com. The results of our experiments confirm that the proposed models outperform other well-known baseline models in detecting fake reviews. To the best of our knowledge, the work discussed in this article represents the first successful attempt to apply text mining methods and semantic language models to the detection of fake consumer reviews. A managerial implication of our research is that firms can apply our design artifacts to monitor online consumer reviews to develop effective marketing or product design strategies based on genuine consumer feedback posted to the Internet.

Mining a data reasoning model for personalized text classification

It is a big challenge to acquire correct user profiles for personalized text classification since users may be unsure in providing their interests. Traditional approaches to user profiling adopt machine learning (ML) to automatically discover classification knowledge from explicit user feedback in describing personal interests. However, the accuracy of ML-based methods cannot be significantly improved in many cases due to the term independence assumption and uncertainties associated with them. This paper presents a novel relevance feedback approach for personalized text classification. It basically applies data mining to discover knowledge from relevant and non-relevant text and constraints specific knowledge by reasoning rules to eliminate some conflicting information. We also developed a Dempster-Shafer (DS) approach as the means to utilise the specific knowledge to build high-quality data models for classification. The experimental results conducted on Reuters Corpus Volume 1 and TREC topics support that the proposed technique achieves encouraging performance in comparing with the state-of-the-art relevance feedback models.

Australian Tax Analysis [8th Edition]

Now in its eighth edition, Australian Tax Analysis: Cases, Commentary, Commercial Applications and Questions has a proven track record as a high level work for students of taxation law written by a team of authors with many years of experience. Taking into account the fact that the volume of material needed to be processed by today’s taxation student can be overwhelming, the well-chosen extracts and thought-provoking commentary in Australian Tax Analysis, 8th edition, provide readers with the depth of knowledge, and reasoning and analytical skills that will be required of them as practitioners. As well as the carefully selected case extracts and the helpful commentary, each chapter is supplemented by engaging practice questions, involving problem-solving, commercial decision-making, legal analysis and quantitative application. All these elements combined make Australian Tax Analysis an invaluable aid to the understanding of a subject that can be both technical and complex.

Principles of taxation law (4th Edition)

Taxation law can be an incredibly complex subject to absorb, particularly when time is limited. Written specifically for students, Principles of Taxation Law 2011 brings much needed clarity to this area of law. Utilising many methods to make this often daunting subject achievable, particular features of the 2011 edition include: • seven parts: overview and structure, principles of income, deductions and offsets, timing issues, investment and business entities, tax avoidance and administration, and indirect taxes; • clearly structured chapters within those parts grouped under helpful headings; • flowcharts, diagrams and tables, end of chapter practice questions, and case summaries; • an appendix containing all of the up to date and relevant rates; and • the online self-testing component mentor, which provides questions for students of both business and law. Every major aspect of the Australian tax system is covered, with chapters on topics such as goods and services tax, superannuation, offsets, partnerships, capital gains tax, trusts, company tax and tax administration. All chapters have been thoroughly revised. Principles of Taxation Law 2011 is the perfect tool to guide the reader from their initial exposure to the subject to success in taxation law exams. [from publisher's website]

Australian tax analysis - cases, commentary, commercial applications and questions [7th Ed.]

"Australian Tax Analysis, seventh edition, provides a comprehensive examination of taxation law with a practical commercial perspective. The seventh edition of this text features: two new chapters: "Offsets" and "Superannuation and Employer Responsibilities"; selected case extracts; Tax Commissioner Rulings; thought-provoking commentary; instruction on how to read the Acts; and engaging problem-based practice questions."--Publisher's website.

Principles of taxation law [2nd Ed.]

"With its clear and concise explanations of taxation law concepts, Principles of Taxation Law 2009 is the ideal text for students studying this complex subject. It covers all major topics underpinning the Australian tax system, including income, deductions, capital gains, tax accounting, international issues, fringe benefits, tax administration, goods and services tax and, in this new edition, offsets and superannuation. Importantly, the book commences with a special chapter on how to study tax law and succeed in taxation law exams."--Publisher description.

Loss and grief at the heart of text messages [REVIEW]

WHAT if you lost someone you loved? What if you had to let go for the sake of your own sanity? Lachlan Philpott's Colder and Dennis Kelly's Orphans, playing as part of La Boite's and Queensland Theatre Company's independents programs, are emotionally and textually dense theatrical works...

Unsupervised multi-label text classification using a world knowledge ontology

The development of text classification techniques has been largely promoted in the past decade due to the increasing availability and widespread use of digital documents. Usually, the performance of text classification relies on the quality of categories and the accuracy of classifiers learned from samples. When training samples are unavailable or categories are unqualified, text classification performance would be degraded. In this paper, we propose an unsupervised multi-label text classification method to classify documents using a large set of categories stored in a world ontology. The approach has been promisingly evaluated by compared with typical text classification methods, using a real-world document collection and based on the ground truth encoded by human experts.

Text mining in negative relevance feedback

It is a big challenge to clearly identify the boundary between positive and negative streams. Several attempts have used negative feedback to solve this challenge; however, there are two issues for using negative relevance feedback to improve the effectiveness of information filtering. The first one is how to select constructive negative samples in order to reduce the space of negative documents. The second issue is how to decide noisy extracted features that should be updated based on the selected negative samples. This paper proposes a pattern mining based approach to select some offenders from the negative documents, where an offender can be used to reduce the side effects of noisy features. It also classifies extracted features (i.e., terms) into three categories: positive specific terms, general terms, and negative specific terms. In this way, multiple revising strategies can be used to update extracted features. An iterative learning algorithm is also proposed to implement this approach on RCV1, and substantial experiments show that the proposed approach achieves encouraging performance.

The role of markup in the digital humanities

The digital humanities are growing rapidly in response to a rise in Internet use. What humanists mostly work on, and which forms much of the contents of our growing repositories, are digital surrogates of originally analog artefacts. But is the data model upon which many of those surrogates are based – embedded markup – adequate for the task? Or does it in fact inhibit reusability and flexibility? To enhance interoperability of resources and tools, some changes to the standard markup model are needed. Markup could be removed from the text and stored in standoff form. The versions of which many cultural heritage texts are composed could also be represented externally, and computed automatically. These changes would not disrupt existing data representations, which could be imported without significant data loss. They would also enhance automation and ease the increasing burden on the modern digital humanist.

Child, Adolescent and Family Development [3rd ed.]

"This book explores the foundations of modern developmental thought, incorporating the latest in international research set within a cultural and historical context. Richly illustrated and enhanced by a range of practical teaching resources, this clear and engaging text is intended to reach students across a range of teaching, psychology, social science and health science disciplines. By employing a thematic approach within the chronologically ordered chapters, this text offers a systematic and intuitive structure for both learning and teaching. This new edition features a set of fully updated case studies that consider current trends and issues in developmental theory and practice, as well as end-of-chapter sections that address important stages in the family life cycle."--publisher website

Teaching in Inclusive School Communities

"Teaching in Inclusive School Communities, 1st Edition is the essential resource to provide pre-service teachers with the most contemporary, ethical and useful framework for incorporating diversity and inclusive practices in today’s classroom. Fourteen concise chapters compose a focused picture of the values and beliefs that inform the inclusive education approach, with the most up-to-date connections to curriculum and pedagogy throughout. Complemented by the latest research in the field, this text provides the practical knowledge and skills needed for inclusive classroom teaching in Australia and New Zealand, as well as a thorough analysis of exactly what is required to build respectful relationships in modern school communities."--publisher website