Personalization in tag ontology learning for recommendation making

Due to the explosive growth of the Web, the domain of Web personalization has gained great momentum both in the research and commercial areas. One of the most popular web personalization systems is recommender systems. In recommender systems choosing user information that can be used to profile users is very crucial for user profiling. In Web 2.0, one facility that can help users organize Web resources of their interest is user tagging systems. Exploring user tagging behavior provides a promising way for understanding users’ information needs since tags are given directly by users. However, free and relatively uncontrolled vocabulary makes the user self-defined tags lack of standardization and semantic ambiguity. Also, the relationships among tags need to be explored since there are rich relationships among tags which could provide valuable information for us to better understand users. In this paper, we propose a novel approach for learning tag ontology based on the widely used lexical database WordNet for capturing the semantics and the structural relationships of tags. We present personalization strategies to disambiguate the semantics of tags by combining the opinion of WordNet lexicographers and users’ tagging behavior together. To personalize further, clustering of users is performed to generate a more accurate ontology for a particular group of users. In order to evaluate the usefulness of the tag ontology, we use the tag ontology in a pilot tag recommendation experiment for improving the recommendation performance by exploiting the semantic information in the tag ontology. The initial result shows that the personalized information has improved the accuracy of the tag recommendation.

The effects of linguistic factors on student performance on economics multiple choice questions

This paper proposes a framework to analyse performance on multiple choice questions with the focus on linguistic factors. Item Response Theory (IRT) is deployed to estimate ability and question difficulty levels. A logistic regression model is used to detect Differential Item Functioning questions. Probit models testify relationships between performance and linguistic factors controlling the effects of question construction and students’ background. Empirical results have important implications. The lexical density of stems affects performance. The use of non-Economics specialised vocabulary has differing impacts on the performance of students with different language backgrounds. The IRT-based ability and difficulty help explain performance variations.

Towards an integrative framework for future research on small firm growth

Our review has demonstrated that small firm growth is a complex phenomenon. The concept ‘growth’ denotes both a change in amount and the process by which that change is attained. Further, the growth can be achieved in different ways and with varying degrees of regularity, and it manifests itself along several different dimensions such as sales, employment, and accumulation of assets. This complexity has naturally led researchers to adopt different approaches to studying growth and to use different measures to assess it. Further, although our review shows that it can fruitfully be regarded as a growth issue, the research on small firms' internationalization has largely developed as a separate stream. Similarly, other relatively separate literatures have evolved, which effectively focus on different modes of growth although mostly without regarding the studies first and foremost as growth studies. This goes for topics such as mergers and acquisitions, diversification, and integration - research streams which have largely ignored the particularities of small firms and which in turn have been largely ignored among researchers focusing on small firm growth.

Preparing for performance capture #1

This video article articulates two exercises that have been developed to respond to the need for preparedness in the growing field of Performance Capture. The first is called Walking Through (Delbridge 2013), where the actor navigates a series of objects that exist in screen space through a developed sense of the existing physical particularities of the studio and an interaction with a screen (or feedback loop). The second exercise is called The Donut (Delbridge 2013), where the performer continues to navigate screen space but this time does so through the literal stepping through of a Torus in the virtual – again with nothing but the studio infrastructure and the screen as a guide. Notions of Motion Captured performance infer the existence of an interface that combines performer with system, separating (or intervening in) the space between performance and the screen. It is precisely the effect and provided opportunity of the intermediary device on the practice, craft and preparedness of the actor as they navigate the connection between 3D screen space and the physical properties of the studio that is examined here. Defining the scope of current practice for the contemporary actor is a key construct of this challenge, with the most appropriate definition revolving around the provision of a required mixture of performance and content for live, mediated, framed and variously captured formats. One of these particular formats is Performance Capture. The exercises presented here are two from a series, developed over a three year study that contribute to our understanding of the potential for a training regimen to be developed for the rigors of Performance Capture.

The pre-socialization of future journalists

While the role of university journalism education in the professionalization of journalists has been extensively debated, systematic and comparative studies of journalism students are still scarce. This paper reports the findings from a comparative study of journalism students in seven countries: Australia, Brazil, Chile, Mexico, Spain, Switzerland, and the United States. The data show a number of similarities, but also important differences between pre-professional cultures in journalism around the world. The findings are in line with recent conceptualizations of media systems, although some variations and particularities are observed at the country level. While students in all countries reject a loyal approach and favor a citizen-oriented role, they also do so to different extents. Brazilian and Chilean students believe in the citizen-oriented and watchdog roles, whereas their counterparts in Australia, Switzerland, and the United States favor the consumer-oriented approach to a greater extent. Mexican and Spanish students, on the other hand, while supporting the citizen-oriented role, reject the loyal role comparatively less than the rest of the countries.

Behavioural Inhibition System response to conflicting advertisement cues: Road safety messages vs. motor vehicle promotional advertisements

This study assessed the revised Behavioural Inhibition System (BIS), as conceptualised by Gray and McNaughton’s (2000) revised RST, by exposing participants to a loss-framed road safety message (emphasising the negative consequences of speeding behaviour) and a high performance motor vehicle promotional advertisement. Licensed young drivers (N = 40, aged 17–25 years) were randomly allocated to view either the message or both the message and advertisement. Participants then completed a computerised lexical decision task prior to completing three personality measures: Corr-Cooper RST-PQ, CARROT and Q-Task. It was predicted that those with a stronger BIS would demonstrate greater processing of these mixed message cues compared to weaker BIS individuals, and that this BIS effect would only be observed in the mixed cues condition (due to simultaneous activation of the incentive and punishment systems). Preliminary findings will be discussed in the context of the influence of personality traits on health message processing.

Processing biases towards gain-framed messages may lead reward sensitive individuals to adopt safer driving behaviours

Using Gray and McNaughton’s revised RST, this study investigated the extent to which the Behavioural Approach System (BAS) and the Fight-Flight-Freeze System (FFFS) influence the processing of gain-framed and loss-framed road safety messages and subsequent message acceptance. It was predicted that stronger BAS sensitivity and FFFS sensitivity would be associated with greater processing and acceptance of the gain-framed messages and loss-framed messages, respectively. Young drivers (N = 80, aged 17–25 years) viewed one of four road safety messages and completed a lexical decision task to assess message processing. Both self-report (e.g., Corr-Cooper RST-PQ) and behavioural measures (i.e., CARROT and Q-Task) were used to assess BAS and FFFS traits. Message acceptance was measured via self-report ratings of message effectiveness, behavioural intentions, attitudes and subsequent driving behaviour. The results are discussed in the context of the effect that differences in reward and punishment sensitivities may have on message processing and message acceptance.

Current trends and issues in geographical education

In this introductory chapter to Schmeinck, D. and Lidstone, J. (2014) “Current trends and issues in geographical education” in Schmeinck, D. and Lidstone, J. (2014) Eds) Standards and Research in Geographical Education: Current Trends and International Issues. Berlin. Mensch und Buch Verlag. Pp. 5 - 16. , the authors review and analyse eleven papers originally presented to the Congress of the International Geographical Union held in Cologne in 2012. Taking the collection of papers as a single corpus representing the “state of the art” of geography education, they applied lexical and bibliometric analyses in an innovative attempt to identify the nature of geographical education as represented by this anthology of peer reviewed chapters presented at the start of the second decade of the Twenty-first century?

Automatic de-identification of electronic health records : an Australian perspective

We present an approach to automatically de-identify health records. In our approach, personal health information is identified using a Conditional Random Fields machine learning classifier, a large set of linguistic and lexical features, and pattern matching techniques. Identified personal information is then removed from the reports. The de-identification of personal health information is fundamental for the sharing and secondary use of electronic health records, for example for data mining and disease monitoring. The effectiveness of our approach is first evaluated on the 2007 i2b2 Shared Task dataset, a widely adopted dataset for evaluating de-identification techniques. Subsequently, we investigate the robustness of the approach to limited training data; we study its effectiveness on different type and quality of data by evaluating the approach on scanned pathology reports from an Australian institution. This data contains optical character recognition errors, as well as linguistic conventions that differ from those contained in the i2b2 dataset, for example different date formats. The findings suggest that our approach compares to the best approach from the 2007 i2b2 Shared Task; in addition, the approach is found to be robust to variations of training size, data type and quality in presence of sufficient training data.

De-Identification of health records using Anonym: Effectiveness and robustness across datasets

Objective Evaluate the effectiveness and robustness of Anonym, a tool for de-identifying free-text health records based on conditional random fields classifiers informed by linguistic and lexical features, as well as features extracted by pattern matching techniques. De-identification of personal health information in electronic health records is essential for the sharing and secondary usage of clinical data. De-identification tools that adapt to different sources of clinical data are attractive as they would require minimal intervention to guarantee high effectiveness. Methods and Materials The effectiveness and robustness of Anonym are evaluated across multiple datasets, including the widely adopted Integrating Biology and the Bedside (i2b2) dataset, used for evaluation in a de-identification challenge. The datasets used here vary in type of health records, source of data, and their quality, with one of the datasets containing optical character recognition errors. Results Anonym identifies and removes up to 96.6% of personal health identifiers (recall) with a precision of up to 98.2% on the i2b2 dataset, outperforming the best system proposed in the i2b2 challenge. The effectiveness of Anonym across datasets is found to depend on the amount of information available for training. Conclusion Findings show that Anonym compares to the best approach from the 2006 i2b2 shared task. It is easy to retrain Anonym with new datasets; if retrained, the system is robust to variations of training size, data type and quality in presence of sufficient training data.

Semantic interference in object naming : an fMRI study of the postcue naming paradigm

It is well established that the time to name target objects can be influenced by the presence of categorically related versus unrelated distractor items. A variety of paradigms have been developed to determine the level at which this semantic interference effect occurs in the speech production system. In this study, we investigated one of these tasks, the postcue naming paradigm, for the first time with fMRI. Previous behavioural studies using this paradigm have produced conflicting interpretations of the processing level at which the semantic interference effect takes place, ranging from pre- to post-lexical. Here we used fMRI with a sparse, event-related design to adjudicate between these competing explanations. We replicated the behavioural postcue naming effect for categorically related target/distractor pairs, and observed a corresponding increase in neuronal activation in the right lingual and fusiform gyri-regions previously associated with visual object processing and colour-form integration. We interpret these findings as being consistent with an account that places the semantic interference effect in the postcue paradigm at a processing level involving integration of object attributes in short-term memory.

Semantic context and visual feature effects in object naming : an fMRI study using arterial spin labeling

Previous behavioral studies reported a robust effect of increased naming latencies when objects to be named were blocked within semantic category, compared to items blocked between category. This semantic context effect has been attributed to various mechanisms including inhibition or excitation of lexico-semantic representations and incremental learning of associations between semantic features and names, and is hypothesized to increase demands on verbal self-monitoring during speech production. Objects within categories also share many visual structural features, introducing a potential confound when interpreting the level at which the context effect might occur. Consistent with previous findings, we report a significant increase in response latencies when naming categorically related objects within blocks, an effect associated with increased perfusion fMRI signal bilaterally in the hippocampus and in the left middle to posterior superior temporal cortex. No perfusion changes were observed in the middle section of the left middle temporal cortex, a region associated with retrieval of lexical-semantic information in previous object naming studies. Although a manipulation of visual feature similarity did not influence naming latencies, we observed perfusion increases in the perirhinal cortex for naming objects with similar visual features that interacted with the semantic context in which objects were named. These results provide support for the view that the semantic context effect in object naming occurs due to an incremental learning mechanism, and involves increased demands on verbal self-monitoring.

Using textual analysis with Concept Inventories to identify root causes of misconceptions

Engineers must have deep and accurate conceptual understanding of their field and Concept inventories (CIs) are one method of assessing conceptual understanding and providing formative feedback. Current CI tests use Multiple Choice Questions (MCQ) to identify misconceptions and have undergone reliability and validity testing to assess conceptual understanding. However, they do not readily provide the diagnostic information about students’ reasoning and therefore do not effectively point to specific actions that can be taken to improve student learning. We piloted the textual component of our diagnostic CI on electrical engineering students using items from the signals and systems CI. We then analysed the textual responses using automated lexical analysis software to test the effectiveness of these types of software and interviewed the students regarding their experience using the textual component. Results from the automated text analysis revealed that students held both incorrect and correct ideas for certain conceptual areas and provided indications of student misconceptions. User feedback also revealed that the inclusion of the textual component is helpful to students in assessing and reflecting on their own understanding.

Text mining with semantic annotation : using enriched text representation for entity-oriented retrieval, semantic relation identification and text clustering

This project is a step forward in the study of text mining where enhanced text representation with semantic information plays a significant role. It develops effective methods of entity-oriented retrieval, semantic relation identification and text clustering utilizing semantically annotated data. These methods are based on enriched text representation generated by introducing semantic information extracted from Wikipedia into the input text data. The proposed methods are evaluated against several start-of-art benchmarking methods on real-life data-sets. In particular, this thesis improves the performance of entity-oriented retrieval, identifies different lexical forms for an entity relation and handles clustering documents with multiple feature spaces.

Using automated text analysis to evaluate students’ conceptual understanding

Background: A major challenge for assessing students’ conceptual understanding of STEM subjects is the capacity of assessment tools to reliably and robustly evaluate student thinking and reasoning. Multiple-choice tests are typically used to assess student learning and are designed to include distractors that can indicate students’ incomplete understanding of a topic or concept based on which distractor the student selects. However, these tests fail to provide the critical information uncovering the how and why of students’ reasoning for their multiple-choice selections. Open-ended or structured response questions are one method for capturing higher level thinking, but are often costly in terms of time and attention to properly assess student responses. Purpose: The goal of this study is to evaluate methods for automatically assessing open-ended responses, e.g. students’ written explanations and reasoning for multiple-choice selections. Design/Method: We incorporated an open response component for an online signals and systems multiple-choice test to capture written explanations of students’ selections. The effectiveness of an automated approach for identifying and assessing student conceptual understanding was evaluated by comparing results of lexical analysis software packages (Leximancer and NVivo) to expert human analysis of student responses. In order to understand and delineate the process for effectively analysing text provided by students, the researchers evaluated strengths and weakness for both the human and automated approaches. Results: Human and automated analyses revealed both correct and incorrect associations for certain conceptual areas. For some questions, that were not anticipated or included in the distractor selections, showing how multiple-choice questions alone fail to capture the comprehensive picture of student understanding. The comparison of textual analysis methods revealed the capability of automated lexical analysis software to assist in the identification of concepts and their relationships for large textual data sets. We also identified several challenges to using automated analysis as well as the manual and computer-assisted analysis. Conclusions: This study highlighted the usefulness incorporating and analysing students’ reasoning or explanations in understanding how students think about certain conceptual ideas. The ultimate value of automating the evaluation of written explanations is that it can be applied more frequently and at various stages of instruction to formatively evaluate conceptual understanding and engage students in reflective