A service-oriented architecture for text analytics enabled business applications

Experiences showed that developing business applications that base on text analysis normally requires a lot of time and expertise in the field of computer linguistics. Several approaches of integrating text analysis systems with business applications have been proposed, but so far there has been no coordinated approach which would enable building scalable and flexible applications of text analysis in enterprise scenarios. In this paper, a service-oriented architecture for text processing applications in the business domain is introduced. It comprises various groups of processing components and knowledge resources. The architecture, created as a result of our experiences with building natural language processing applications in business scenarios, allows for the reuse of text analysis and other components, and facilitates the development of business applications. We verify our approach by showing how the proposed architecture can be applied to create a text analytics enabled business application that addresses a concrete business scenario. © 2010 IEEE.

Burning questions: Exploring the impact of a natural disaster on community pharmacies

Background The past decade has seen a rapid change in the climate system with an increased risk of extreme weather events. On and following the 3rd of January 2013, Tasmania experienced three catastrophic bushfires, which led to the evacuation of several communities, the loss of many properties, and a financial cost of approximately AUD$80 million. Objective To explore the impacts of the 2012/2013 Tasmanian bushfires on community pharmacies. Method Qualitative research methods were undertaken, employing semi-structured telephone interviews with a purposive sample of seven Tasmanian pharmacists. The interviews were recorded and transcribed, and two different methods were used to analyse the text. The first method utilised Leximancer® text analytics software to provide a birds-eye view of the conceptual structure of the text. The second method involved manual, open and axial coding, conducted independently by the two researchers for inter-rater reliability, to identify key themes in the discourse. Results Two main themes were identified - ‘people’ and ‘supply’ - from which six key concepts were derived. The six concepts were ‘patients’, ‘pharmacists’, ‘local doctor’, ‘pharmacy operations’, ‘disaster management planning’, and ‘emergency supply regulation’. Conclusion This study identified challenges faced by community pharmacists during Tasmanian bushfires. Interviewees highlighted the need for both the Tasmanian State Government and the Australian Federal Government to recognise the important primary care role that community pharmacists play during natural disasters, and therefore involve pharmacists in disaster management planning. They called for greater support and guidance for community pharmacists from regulatory and other government bodies during these events. Their comments highlighted the need for a review of Tasmania’s 3-day emergency supply regulation that allows pharmacists to provide a three-day supply of a patient’s medication without a doctor’s prescription in an emergency situation.

Analysing reflective text for learning analytics : an approach using anomaly recontextualisation

Reflective writing is an important learning task to help foster reflective practice, but even when assessed it is rarely analysed or critically reviewed due to its subjective and affective nature. We propose a process for capturing subjective and affective analytics based on the identification and recontextualisation of anomalous features within reflective text. We evaluate 2 human supervised trials of the process, and so demonstrate the potential for an automated Anomaly Recontextualisation process for Learning Analytics.

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

Centroid training to achieve effective text classification

Traditional text classification technology based on machine learning and data mining techniques has made a big progress. However, it is still a big problem on how to draw an exact decision boundary between relevant and irrelevant objects in binary classification due to much uncertainty produced in the process of the traditional algorithms. The proposed model CTTC (Centroid Training for Text Classification) aims to build an uncertainty boundary to absorb as many indeterminate objects as possible so as to elevate the certainty of the relevant and irrelevant groups through the centroid clustering and training process. The clustering starts from the two training subsets labelled as relevant or irrelevant respectively to create two principal centroid vectors by which all the training samples are further separated into three groups: POS, NEG and BND, with all the indeterminate objects absorbed into the uncertain decision boundary BND. Two pairs of centroid vectors are proposed to be trained and optimized through the subsequent iterative multi-learning process, all of which are proposed to collaboratively help predict the polarities of the incoming objects thereafter. For the assessment of the proposed model, F1 and Accuracy have been chosen as the key evaluation measures. We stress the F1 measure because it can display the overall performance improvement of the final classifier better than Accuracy. A large number of experiments have been completed using the proposed model on the Reuters Corpus Volume 1 (RCV1) which is important standard dataset in the field. The experiment results show that the proposed model has significantly improved the binary text classification performance in both F1 and Accuracy compared with three other influential baseline models.

Making sense of an everyday science text : linguistic, visual and spatial design

In this article, we take a close look at the literacy demands of one task from the ‘Marvellous Micro-organisms Stage 3 Life and Living’ Primary Connections unit (Australian Academy of Science, 2005). One lesson from the unit, ‘Exploring Bread’, (pp 4-8) asks students to ‘use bread labels to locate ingredient information and synthesise understanding of bread ingredients’. We draw upon a framework offered by the New London Group (2000), that of linguistic, visual and spatial design, to consider in more detail three bread wrappers and from there the complex literacies that students need to interrelate to undertake the required task. Our findings are that although bread wrappers are an example of an everyday science text, their linguistic, visual and spatial designs and their interrelationship are not trivial. We conclude by reinforcing the need for teachers of science to also consider how the complex design elements of everyday science texts and their interrelated literacies are made visible through instructional practice.

Historical timelines : analyzing multimodal text design

The recent focus on literacy in Social Studies has been on linguistic design, particularly that related to the grammar of written and spoken text. When students are expected to produce complex hybridized genres such as timelines, a focus on the teaching and learning of linguistic design is necessary but not sufficient to complete the task. Theorizations of new literacies identify five interrelated meaning making designs for text deconstruction and reproduction: linguistic, spatial, visual, gestural, and audio design. Honing in on the complexity of timelines, this paper casts a lens on the linguistic, visual, spatial, and gestural designs of three pairs of primary school aged Social Studies learners. Drawing on a functional metalanguage, we analyze the linguistic, visual, spatial, and gestural designs of their work. We also offer suggestions of their effect, and from there consider the importance of explicit instruction in text design choices for this Social Studies task. We conclude the analysis by suggesting the foci of explicit instruction for future lessons.