Adaptive dual control of topic-based information retrieval

Information Retrieval is an important albeit imperfect component of information technologies. A problem of insufficient diversity of retrieved documents is one of the primary issues studied in this research. This study shows that this problem leads to a decrease of precision and recall, traditional measures of information retrieval effectiveness. This thesis presents an adaptive IR system based on the theory of adaptive dual control. The aim of the approach is the optimization of retrieval precision after all feedback has been issued. This is done by increasing the diversity of retrieved documents. This study shows that the value of recall reflects this diversity. The Probability Ranking Principle is viewed in the literature as the “bedrock” of current probabilistic Information Retrieval theory. Neither the proposed approach nor other methods of diversification of retrieved documents from the literature conform to this principle. This study shows by counterexample that the Probability Ranking Principle does not in general lead to optimal precision in a search session with feedback (for which it may not have been designed but is actively used). Retrieval precision of the search session should be optimized with a multistage stochastic programming model to accomplish the aim. However, such models are computationally intractable. Therefore, approximate linear multistage stochastic programming models are derived in this study, where the multistage improvement of the probability distribution is modelled using the proposed feedback correctness method. The proposed optimization models are based on several assumptions, starting with the assumption that Information Retrieval is conducted in units of topics. The use of clusters is the primary reasons why a new method of probability estimation is proposed. The adaptive dual control of topic-based IR system was evaluated in a series of experiments conducted on the Reuters, Wikipedia and TREC collections of documents. The Wikipedia experiment revealed that the dual control feedback mechanism improves precision and S-recall when all the underlying assumptions are satisfied. In the TREC experiment, this feedback mechanism was compared to a state-of-the-art adaptive IR system based on BM-25 term weighting and the Rocchio relevance feedback algorithm. The baseline system exhibited better effectiveness than the cluster-based optimization model of ADTIR. The main reason for this was insufficient quality of the generated clusters in the TREC collection that violated the underlying assumption.

Languaging the actor : an examination of the terminology used in actor training

Throughout the twentieth century increased interest in the training of actors resulted in the emergence of a plethora of acting theories and innovative theatrical movements in Europe, the UK and the USA. The individuals or groups involved with the formulation of these theories and movements developed specific terminologies, or languages of acting, in an attempt to clearly articulate the nature and the practice of acting according to their particular pedagogy or theatrical aesthetic. Now at the dawning of the twenty-first century, Australia boasts quite a number of schools and university courses professing to train actors. This research aims to discover the language used in actor training on the east coast of Australia today. Using interviews with staff of the National Institute of Dramatic Art, the Victorian College of the Arts, and the Queensland University of Technology as the primary source of data, a constructivist grounded theory has emerged to assess the influence of last century‟s theatrical theorists and practitioners on Australian training and to ascertain the possibility of a distinctly Australian language of acting.

Investigating the performance of automatic new topic identification across multiple datasets 1

Recent studies on automatic new topic identification in Web search engine user sessions demonstrated that neural networks are successful in automatic new topic identification. However most of this work applied their new topic identification algorithms on data logs from a single search engine. In this study, we investigate whether the application of neural networks for automatic new topic identification are more successful on some search engines than others. Sample data logs from the Norwegian search engine FAST (currently owned by Overture) and Excite are used in this study. Findings of this study suggest that query logs with more topic shifts tend to provide more successful results on shift-based performance measures, whereas logs with more topic continuations tend to provide better results on continuation-based performance measures.

Effect of topic domain and task type on Web image searching

Many user studies in Web information searching have found the significant effect of task types on search strategies. However, little attention was given to Web image searching strategies, especially the query reformulation activity despite that this is a crucial part in Web image searching. In this study, we investigated the effects of topic domains and task types on user’s image searching behavior and query reformulation strategies. Some significant differences in user’s tasks specificity and initial concepts were identified among the task domains. Task types are also found to influence participant’s result reviewing behavior and query reformulation strategies.

Mastering medical terminology : Australia and New Zealand

Mastering Medical Terminology: Australia and New Zealand is medical terminology book of relevance to an audience in Australia and New Zealand. Australian terminology, perspectives, examples and spelling have been included and Australian pronunciation specified. The textbook is accompanied by a self-help workbook, an online workbook and a Smartphone app. Throughout Mastering Medical Terminology, review of medical terminology as it is used in clinical practice is highlighted. Features of the textbook, workbook and electronic product include: • Simple, non-technical explanations of medical terms • Workbook format with ample spaces to write answers • Explanations of clinical procedures, laboratory tests and abbreviations used in Australian clinical practice, as they apply to each body system and speciality area • Pronunciation of terms and spaces to write meanings of terms • Practical applications sections • Exercises that test understanding of terminology as students work through the text chapter by chapter • Review activities that pull together terminology to help students study • Comprehensive glossary and appendices for reference • Links to other useful references, such as websites and textbooks.

Mastering medical terminology : Australia and New Zealand Workbook

Mastering Medical Terminology: Australia and New Zealand Workbook is the indispensable companion to Mastering Medical Terminology Textbook. Packed with a range of exercises and activities to accompany the main text, the Workbook provides an ideal resource for self-testing and revision in a fun, practical and accessible format, and forms a key part of the Mastering Medical Terminology suite of products which are all available for separate purchase enabling you to pick and choose the right package for your learning requirements. Featuring a variety of question types including crossword puzzles, anagrams, multiple-choice questions and label-the-diagram exercises, the Workbook uses entirely Australian spelling and aligns to the chapters of the main text. When used in combination with the main text and MedWords app, Mastering Medical Terminology: Australia and New Zealand Workbook will make the scholarship of medical terminology not only manageable, but fun!

Licensing age issues : deliberations from a workshop devoted to this topic

Objective: To highlight the issues and discuss the research evidence regarding safety, mobility, and other consequences of different licensing ages. Methods: Information included is based on presentations and discussions at a one-day workshop on licensing age issues, and a review and synthesis of the international literature. Results: The literature indicates that higher licensing ages are associated with safety benefits. There is an associated mobility loss, more likely to be an issue in rural states. Legislative attempts to raise the minimum age for independent driving in the United States, e.g., from 16 to 17, have been resisted, although in some states the age has been raised indirectly through graduated driver licensing (GDL) policies. Conclusions: Jurisdictions can achieve reductions in teenage crashes by raising the licensing age. This can be done directly, or indirectly by strengthening GDL systems, in particular extending the minimum length of the learner period.

Time-aware topic recommendation based on micro-blogs

Topic recommendation can help users deal with the information overload issue in micro-blogging communities. This paper proposes to use the implicit information network formed by the multiple relationships among users, topics and micro-blogs, and the temporal information of micro-blogs to find semantically and temporally relevant topics of each topic, and to profile users' time-drifting topic interests. The Content based, Nearest Neighborhood based and Matrix Factorization models are used to make personalized recommendations. The effectiveness of the proposed approaches is demonstrated in the experiments conducted on a real world dataset that collected from Twitter.com.

Hot topic detection in news blogs from the perspective of W2T

News blog hot topics are important for the information recommendation service and marketing. However, information overload and personalized management make the information arrangement more difficult. Moreover, what influences the formation and development of blog hot topics is seldom paid attention to. In order to correctly detect news blog hot topics, the paper first analyzes the development of topics in a new perspective based on W2T (Wisdom Web of Things) methodology. Namely, the characteristics of blog users, context of topic propagation and information granularity are unified to analyze the related problems. Some factors such as the user behavior pattern, network opinion and opinion leader are subsequently identified to be important for the development of topics. Then the topic model based on the view of event reports is constructed. At last, hot topics are identified by the duration, topic novelty, degree of topic growth and degree of user attention. The experimental results show that the proposed method is feasible and effective.

A two-stage approach for generating topic models

Topic modeling has been widely utilized in the fields of information retrieval, text mining, text classification etc. Most existing statistical topic modeling methods such as LDA and pLSA generate a term based representation to represent a topic by selecting single words from multinomial word distribution over this topic. There are two main shortcomings: firstly, popular or common words occur very often across different topics that bring ambiguity to understand topics; secondly, single words lack coherent semantic meaning to accurately represent topics. In order to overcome these problems, in this paper, we propose a two-stage model that combines text mining and pattern mining with statistical modeling to generate more discriminative and semantic rich topic representations. Experiments show that the optimized topic representations generated by the proposed methods outperform the typical statistical topic modeling method LDA in terms of accuracy and certainty.

Pattern-based topic models for information filtering

Topic modelling, such as Latent Dirichlet Allocation (LDA), was proposed to generate statistical models to represent multiple topics in a collection of documents, which has been widely utilized in the fields of machine learning and information retrieval, etc. But its effectiveness in information filtering is rarely known. Patterns are always thought to be more representative than single terms for representing documents. In this paper, a novel information filtering model, Pattern-based Topic Model(PBTM) , is proposed to represent the text documents not only using the topic distributions at general level but also using semantic pattern representations at detailed specific level, both of which contribute to the accurate document representation and document relevance ranking. Extensive experiments are conducted to evaluate the effectiveness of PBTM by using the TREC data collection Reuters Corpus Volume 1. The results show that the proposed model achieves outstanding performance.

Academic legal research and academic research capacity enhancement working topic : "purposes and objectives of law schools beyond educating students (research, capacity building,community service and outreach)"

One of the main objectives of law schools beyond educating students is to produce viable legal research. The comments in this paper are basically confined to the Australian context, and to examine this topic effectively, it is necessary to briefly review the current tertiary research agenda in Australia. This paper argues that there is a need for recognition and support for an expanded legal research framework along with additional research training for legal academics. There also needs to be more effective methods of measuring and recognising quality in legal research. This method needs to be one that can engender respect in an interdisciplinary context.

Integrating time forgetting mechanisms into topic-based user interest profiling

The rapid development of the World Wide Web has created massive information leading to the information overload problem. Under this circumstance, personalization techniques have been brought out to help users in finding content which meet their personalized interests or needs out of massively increasing information. User profiling techniques have performed the core role in this research. Traditionally, most user profiling techniques create user representations in a static way. However, changes of user interests may occur with time in real world applications. In this research we develop algorithms for mining user interests by integrating time decay mechanisms into topic-based user interest profiling. Time forgetting functions will be integrated into the calculation of topic interest measurements on in-depth level. The experimental study shows that, considering temporal effects of user interests by integrating time forgetting mechanisms shows better performance of recommendation.

Mastering medical terminology online workbook

Key Features: "Aligns to Mastering Medical Terminology: Australia and New Zealand. Self-test to practise and revise combining forms, word roots, prefixes and suffixes. Crossword puzzles, anagrams and label-the-diagram exercises Also available iOS/Android app featuring audio glossary, flashcards and MCQs."--publisher website