Statistical Analysis of Text Summarization Evaluation

This dissertation applies statistical methods to the evaluation of automatic summarization using data from the Text Analysis Conferences in 2008-2011. Several aspects of the evaluation framework itself are studied, including the statistical testing used to determine significant differences, the assessors, and the design of the experiment. In addition, a family of evaluation metrics is developed to predict the score an automatically generated summary would receive from a human judge and its results are demonstrated at the Text Analysis Conference. Finally, variations on the evaluation framework are studied and their relative merits considered. An over-arching theme of this dissertation is the application of standard statistical methods to data that does not conform to the usual testing assumptions.

Dynamic networks, text analysis and Gephi: the art math

In numerosi campi scientici l'analisi di network complessi ha portato molte recenti scoperte: in questa tesi abbiamo sperimentato questo approccio sul linguaggio umano, in particolare quello scritto, dove le parole non interagiscono in modo casuale. Abbiamo quindi inizialmente presentato misure capaci di estrapolare importanti strutture topologiche dai newtork linguistici(Degree, Strength, Entropia, . . .) ed esaminato il software usato per rappresentare e visualizzare i grafi (Gephi). In seguito abbiamo analizzato le differenti proprietà statistiche di uno stesso testo in varie sue forme (shuffolato, senza stopwords e senza parole con bassa frequenza): il nostro database contiene cinque libri di cinque autori vissuti nel XIX secolo. Abbiamo infine mostrato come certe misure siano importanti per distinguere un testo reale dalle sue versioni modificate e perché la distribuzione del Degree di un testo normale e di uno shuffolato abbiano lo stesso andamento. Questi risultati potranno essere utili nella sempre più attiva analisi di fenomeni linguistici come l'autorship attribution e il riconoscimento di testi shuffolati.

Computer-aided System of Semantic Text Analysis of a Technical Specification

The given work is devoted to development of the computer-aided system of semantic text analysis of a technical specification. The purpose of this work is to increase efficiency of software engineering based on automation of semantic text analysis of a technical specification. In work it is offered and investigated the model of the analysis of the text of the technical project is submitted, the attribute grammar of a technical specification, intended for formalization of limited Russian is constructed with the purpose of analysis of offers of text of a technical specification, style features of the technical project as class of documents are considered, recommendations on preparation of text of a technical specification for the automated processing are formulated. The computer-aided system of semantic text analysis of a technical specification is considered. This system consists of the following subsystems: preliminary text processing, the syntactic and semantic analysis and construction of software models, storage of documents and interface.

Computer Support of Semantic Text Analysis of a Technical Specification on Designing Software

The given work is devoted to development of the computer-aided system of semantic text analysis of a technical specification. The purpose of this work is to increase efficiency of software engineering based on automation of semantic text analysis of a technical specification. In work it is offered and investigated a technique of the text analysis of a technical specification is submitted, the expanded fuzzy attribute grammar of a technical specification, intended for formalization of limited Russian language is constructed with the purpose of analysis of offers of text of a technical specification, style features of the technical specification as class of documents are considered, recommendations on preparation of text of a technical specification for the automated processing are formulated. The computer-aided system of semantic text analysis of a technical specification is considered. This system consist of the following subsystems: preliminary text processing, the syntactic and semantic analysis and construction of software models, storage of documents and interface.

Hatalmi távolság és intézményi kollektivizmus – a versenyképesség előrejelzése szövegelemzéssel (Power distance and institutional collectivism – Forecast of competitiveness by text analysis)

A tárgyalófelek elé kitett mobiltelefon alkalmazása előrejelzi a beszélgetőpartnerek versenyképességét a versenyképesség-mutatók alapján, javaslatokat adva a tárgyalás további menetére. Ez a vízió nyilván még futurisztikus, ám a csúcsvezetői nyilatkozatok rejtett szövegtartalma alapján következtetéseket levonni a képviselt szervezetek versenyképességi orientációira – ez már ma lehetőség. A GLOBE-projekt kultúrakutatási módszertanával, valamint szövegelemzési módszerekkel sikerült kimutatni a versenyképességet előrejelző hatalmi távolság és az intézményi kollektivizmus szövegbeli jeleit. Mindez eszközt jelenthet egyebek mellett a szervezetfejlesztéssel, hírszerzéssel, HR-gazdálkodással foglalkozó szakembereknek is. _______ The use of the mobile telephones laid in front of the negotiators during their conversations forecasts their indicators of competitiveness and gives suggestions for the further course of negotiation. This is obviously a futuristic vision, but drawing conclusions from the hidden content of top management narratives concerning the competitive cultural orientations of the represented organizations is a possibility that is already available. Using the culture research methodology of the GLOBE project as well as text analysis methods, it was possible to reveal narrative patterns both of the power distance, forecasting competitiveness, and of institutional collectivism. These findings may be useful tools for professionals, among others of organizational development, intelligence service and HR management.

Do Editorial Policies Support Ethical Research? A Thematic Text Analysis of Author Instructions in Psychiatry Journals

Introduction: According to the Declaration of Helsinki and other guidelines, clinical studies should be approved by a research ethics committee and seek valid informed consent from the participants. Editors of medical journals are encouraged by the ICMJE and COPE to include requirements for these principles in the journal's instructions for authors. This study assessed the editorial policies of psychiatry journals regarding ethics review and informed consent. Methods and Findings: The information given on ethics review and informed consent and the mentioning of the ICMJE and COPE recommendations were assessed within author's instructions and online submission procedures of all 123 eligible psychiatry journals. While 54% and 58% of editorial policies required ethics review and informed consent, only 14% and 19% demanded the reporting of these issues in the manuscript. The TOP-10 psychiatry journals (ranked by impact factor) performed similarly in this regard. Conclusions: Only every second psychiatry journal adheres to the ICMJE's recommendation to inform authors about requirements for informed consent and ethics review. Furthermore, we argue that even the ICMJE's recommendations in this regard are insufficient, at least for ethically challenging clinical trials. At the same time, ideal scientific design sometimes even needs to be compromised for ethical reasons. We suggest that features of clinical studies that make them morally controversial, but not necessarily unethical, are analogous to methodological limitations and should thus be reported explicitly. Editorial policies as well as reporting guidelines such as CONSORT should be extended to support a meaningful reporting of ethical research.

Mutual dependency grid for stakeholder mapping: a component-based approach to supply chain participant analysis

Stakeholder analysis plays a critical role in business analysis. However, the majority of the stakeholder identification and analysis methods focus on the activities and processes and ignore the artefacts being processed by human beings. By focusing on the outputs of the organisation, an artefact-centric view helps create a network of artefacts, and a component-based structure of the organisation and its supply chain participants. Since the relationship is based on the components, i.e. after the stakeholders are identified, the interdependency between stakeholders and the focal organisation can be measured. Each stakeholder is associated with two types of dependency, namely the stakeholder’s dependency on the focal organisation and the focal organisation’s dependency on the stakeholder. We identify three factors for each type of dependency and propose the equations that calculate the dependency indexes. Once both types of the dependency indexes are calculated, each stakeholder can be placed and categorised into one of the four groups, namely critical stakeholder, mutual benefits stakeholder, replaceable stakeholder, and easy care stakeholder. The mutual dependency grid and the dependency gap analysis, which further investigates the priority of each stakeholder by calculating the weighted dependency gap between the focal organisation and the stakeholder, subsequently help the focal organisation to better understand its stakeholders and manage its stakeholder relationships.

Tensor regression based on linked multiway parameter analysis

Classical regression methods take vectors as covariates and estimate the corresponding vectors of regression parameters. When addressing regression problems on covariates of more complex form such as multi-dimensional arrays (i.e. tensors), traditional computational models can be severely compromised by ultrahigh dimensionality as well as complex structure. By exploiting the special structure of tensor covariates, the tensor regression model provides a promising solution to reduce the model’s dimensionality to a manageable level, thus leading to efficient estimation. Most of the existing tensor-based methods independently estimate each individual regression problem based on tensor decomposition which allows the simultaneous projections of an input tensor to more than one direction along each mode. As a matter of fact, multi-dimensional data are collected under the same or very similar conditions, so that data share some common latent components but can also have their own independent parameters for each regression task. Therefore, it is beneficial to analyse regression parameters among all the regressions in a linked way. In this paper, we propose a tensor regression model based on Tucker Decomposition, which identifies not only the common components of parameters across all the regression tasks, but also independent factors contributing to each particular regression task simultaneously. Under this paradigm, the number of independent parameters along each mode is constrained by a sparsity-preserving regulariser. Linked multiway parameter analysis and sparsity modeling further reduce the total number of parameters, with lower memory cost than their tensor-based counterparts. The effectiveness of the new method is demonstrated on real data sets.

Meeting detection in video through semantic analysis

In this paper we present a novel approach to detect people meeting. The proposed approach works by translating people behaviour from trajectory information into semantic terms. Having available a semantic model of the meeting behaviour, the event detection is performed in the semantic domain. The model is learnt employing a soft-computing clustering algorithm that combines trajectory information and motion semantic terms. A stable representation can be obtained from a series of examples. Results obtained on a series of videos with different types of meeting situations show that the proposed approach can learn a generic model that can effectively be applied on the behaviour recognition of meeting situations.

Automated highway systems precursor systems analyses interim results workshop. Conference proceedings.

Turner-Fairbank Highway Research Center, Office of Safety Research and Development, McLean, Va.

Text analytics of social media: Sentiment analysis, event detection and summarization

In the last decade, large numbers of social media services have emerged and been widely used in people's daily life as important information sharing and acquisition tools. With a substantial amount of user-contributed text data on social media, it becomes a necessity to develop methods and tools for text analysis for this emerging data, in order to better utilize it to deliver meaningful information to users. ^ Previous work on text analytics in last several decades is mainly focused on traditional types of text like emails, news and academic literatures, and several critical issues to text data on social media have not been well explored: 1) how to detect sentiment from text on social media; 2) how to make use of social media's real-time nature; 3) how to address information overload for flexible information needs. ^ In this dissertation, we focus on these three problems. First, to detect sentiment of text on social media, we propose a non-negative matrix tri-factorization (tri-NMF) based dual active supervision method to minimize human labeling efforts for the new type of data. Second, to make use of social media's real-time nature, we propose approaches to detect events from text streams on social media. Third, to address information overload for flexible information needs, we propose two summarization framework, dominating set based summarization framework and learning-to-rank based summarization framework. The dominating set based summarization framework can be applied for different types of summarization problems, while the learning-to-rank based summarization framework helps utilize the existing training data to guild the new summarization tasks. In addition, we integrate these techneques in an application study of event summarization for sports games as an example of how to better utilize social media data. ^

Text Analytics of Social Media: Sentiment Analysis, Event Detection and Summarization

In the last decade, large numbers of social media services have emerged and been widely used in people's daily life as important information sharing and acquisition tools. With a substantial amount of user-contributed text data on social media, it becomes a necessity to develop methods and tools for text analysis for this emerging data, in order to better utilize it to deliver meaningful information to users. Previous work on text analytics in last several decades is mainly focused on traditional types of text like emails, news and academic literatures, and several critical issues to text data on social media have not been well explored: 1) how to detect sentiment from text on social media; 2) how to make use of social media's real-time nature; 3) how to address information overload for flexible information needs. In this dissertation, we focus on these three problems. First, to detect sentiment of text on social media, we propose a non-negative matrix tri-factorization (tri-NMF) based dual active supervision method to minimize human labeling efforts for the new type of data. Second, to make use of social media's real-time nature, we propose approaches to detect events from text streams on social media. Third, to address information overload for flexible information needs, we propose two summarization framework, dominating set based summarization framework and learning-to-rank based summarization framework. The dominating set based summarization framework can be applied for different types of summarization problems, while the learning-to-rank based summarization framework helps utilize the existing training data to guild the new summarization tasks. In addition, we integrate these techneques in an application study of event summarization for sports games as an example of how to better utilize social media data.

Evaluating a School Community Linked Physical Activity Intervention Programme: a Multi-level Analysis.

Previous research has highlighted the importance of positive physical activity (PA) behaviors during childhood to promote sustained active lifestyles throughout the lifespan (Telama et al. 2005; 2014). It is in this context that the role of schools and teachers in facilitating PA education is promoted. Research suggests that teachers play an important role in the attitudes of children towards PA (Figley 1985) and schools may be an efficient vehicle for PA provision and promotion (McGinnis, Kanner and DeGraw, 1991; Wechsler, Deveraux, Davis and Collins, 2000). Yet despite consensus that schools represent an ideal setting from which to ‘reach’ young people (Department of Health and Human Services, UK, 2012) there remains conceptual (e.g. multi-component intervention) and methodological (e.g. duration, intensity, family involvement) ambiguity regarding the mechanisms of change claimed by PA intervention programmes. This may, in part, contribute to research findings that suggest that PA interventions have had limited impact on children’s overall activity levels and thereby limited impact in reducing children’s metabolic health (Metcalf, Henley & Wilkin, 2012). A marked criticism of the health promotion field has been the focus on behavioural change while failing to acknowledge the impact of context in influencing health outcomes (Golden & Earp, 2011). For years, the trans-theoretical model of behaviour change has been ‘the dominant model for health behaviour change’ (Armitage, 2009); this model focusses primarily on the individual and the psychology of the change process. Arguably, this model is limited by the individual’s decision-making ability and degree of self-efficacy in order to achieve sustained behavioural change and does not take account of external factors that may hinder their ability to realise change. Similar to the trans-theoretical model, socio-ecological models identify the individual at the focal point of change but also emphasises the importance of connecting multiple impacting variables, in particular, the connections between the social environment, the physical environment and public policy in facilitating behavioural change (REF). In this research, a social-ecological framework was used to connect the ways a PA intervention programme had an impact (or not) on participants, and to make explicit the foundational features of the programme that facilitated positive change. In this study, we examined the evaluation of a multi-agency approach to a PA intervention programme which aimed to increase physical activity, and awareness of the importance of physical activity to key stage 2 (age 7-12) pupils in three UK primary schools. The agencies involved were the local health authority, a community based charitable organisation, a local health administrative agency, and the city school district. In examining the impact of the intervention, we adopted a process evaluation model in order to better understand the mechanisms and context that facilitated change. Therefore, the aim of this evaluation was to describe the provision, process and impact of the intervention by 1) assessing changes in physical activity levels 2) assessing changes in the student’s attitudes towards physical activity, 3) examining student’s perceptions of the child size fitness equipment in school and their likelihood of using the equipment outside of school and 4) exploring staff perceptions, specifically the challenges and benefits, of facilitating equipment based exercise sessions in the school environment. Methodology, Methods, Research Instruments or Sources Used Evaluation of the intervention was designed as a matched-control study and was undertaken over a seven-month period. The school-based intervention involved 3 intervention schools (n =436; 224 boys) and one control school (n=123; 70 boys) in a low socioeconomic and multicultural urban setting. The PA intervention was separated into two phases: a motivation DVD and 10 days of circuit based exercise sessions (Phase 1) followed by a maintenance phase (Phase 2) that incorporated a PA reward program and the use of specialist kid’s gym equipment located at each school for a period of 4 wk. Outcome measures were measured at baseline (January) and endpoint (July; end of academic school year) using reliable and valid self-report measures. The children’s attitudes towards PA were assessed using the Children’s Attitudes towards Physical Activity (CATPA) questionnaire. The Physical Activity Questionnaire for Children (PAQ-C), a 7-day recall questionnaire, was used to assess PA levels over a school week. A standardised test battery (Fitnessgram®) was used to assess cardiovascular fitness, body composition, muscular strength and endurance, and flexibility. After the 4 wk period, similar kid’s equipment was available for general access at local community facilities. The control school did not receive any of the interventions. All physical fitness tests and PA questionnaires were administered and collected prior to the start of the intervention (January) and following the intervention period (July) by an independent evaluation team. Evaluation testing took place at the individual schools over 2-3 consecutive days (depending on the number of children to be tested at the school). Staff (n=19) and student perceptions (n = 436) of the child sized fitness equipment were assessed via questionnaires post-intervention. Students completed a questionnaire to assess enjoyment, usage, ease of use and equipment assess and usage in the community. A questionnaire assessed staff perceptions on the delivery of the exercise sessions, classroom engagement and student perceptions. Conclusions, Expected Outcomes or Findings Findings showed that both the intervention (16.4%) and control groups increased their PAQ-C score by post-intervention (p < 0.05); with the intervention (17.8%) and control (21.3%) boys showing the greatest increase in physical activity levels. At post-intervention, there was a 5.5% decline in the intervention girls’ attitudes toward PA in the aesthetic subdomains (p = 0.009); whereas the control boys had an increase in positive attitudes in the health domain (p = 0.003). No significant differences in attitudes towards physical activity were observed in any other domain for either group at post-intervention (p > 0.05). The results of the equipment questionnaire, 96% of the children stated they enjoyed using the equipment and would like to use the equipment again in the future; however at post-intervention only 27% reported using the equipment outside of school in the last 7 days. Students identified the ski walker (34%) and cycle (32%) as their favorite pieces of equipment; with the single joint exercises such as leg extension and bicep/tricep machine (<3%) as their least favorite. Key themes from staff were that the equipment sessions were enjoyable, a novel activity, children felt very grown-up, and the activity was linked to a real fitness experience. They also expressed the need for more support to deliver the sessions and more time required for each session. Findings from this study suggest that a more integrated approach within the various agencies is required, particularly more support to increase teachers pedagogical content knowledge in physical activity instruction which is age appropriate. Future recommendations for successful implementation include sufficient time period for all students to access and engage with the equipment; increased access and marketing of facilities to parents within the local community, and professional teacher support strategies to facilitate the exercise sessions.