Towards the TopMost: A Topic Modeling System Toolkit

Topic models have been proposed for decades with various applications and recently refreshed by the neural variational inference. However, these topic models adopt totally distinct dataset, implementation, and evaluation settings, which hinders their quick utilization and fair comparisons. This greatly hinders the research progress of topic models. To address these issues, in this paper we propose a Topic Modeling System Toolkit (TopMost). Compared to existing toolkits, TopMost stands out by covering a wider range of topic modeling scenarios including complete lifecycles with dataset pre-processing, model training, testing, and evaluations. The highly cohesive and decoupled modular design of TopMost enables quick utilization, fair comparisons, and flexible extensions of different topic models. This can facilitate the research and applications of topic models. Our code, tutorials, and documentation are available at https://github.com/bobxwu/topmost

Finding answers to questions, in text collections or web, in open domain or specialty domains

International audienceThis chapter is dedicated to factual question answering, i.e. extracting precise and exact answers to question given in natural language from texts. A question in natural language gives more information than a bag of word query (i.e. a query made of a list of words), and provides clues for finding precise answers. We will first focus on the presentation of the underlying problems mainly due to the existence of linguistic variations between questions and their answerable pieces of texts for selecting relevant passages and extracting reliable answers. We will first present how to answer factual question in open domain. We will also present answering questions in specialty domain as it requires dealing with semi-structured knowledge and specialized terminologies, and can lead to different applications, as information management in corporations for example. Searching answers on the Web constitutes another application frame and introduces specificities linked to Web redundancy or collaborative usage. Besides, the Web is also multilingual, and a challenging problem consists in searching answers in target language documents other than the source language of the question. For all these topics, we present main approaches and the remaining problems

Knowledge Management in Higher Education: Effectiveness, Success factors, and Organisational Performance

In today business environment, Higher education institutions are facing a common challenge in the wake of rapid changes due to substantial drops in public funding for public colleges and universities, a larger number of calls for transparency, rapid expansion of the global business. To survive, organizations of higher education must improve their performance continually. Researchers reported that knowledge and effectively managing knowledge can help HEIs improve their performance by solving many of these problems and acquire and sustain competitive advantage. It is beneficial to explore the factors that impact the effective implementation of knowledge management within higher education institutions. These factors are organizational culture, and leadership styles. Additionally, it is essential to investigate the leadership style that best supports effective implementation of knowledge management. This study sought to examine the relationship between organizational culture (mission, adaptability, involvement, consistency), leadership styles (transformational and transactional), knowledge management effectiveness, and organizational performance. The study also analyzed the mediating role of organizational culture on the relationship between leadership styles and knowledge management effectiveness. Based on existing literature, eight hypotheses and a conceptual model were developed regarding the relationships of the five constructs: organizational culture, transformational and transformational leadership, knowledge management effectiveness and organizational performance. All constructs are measured by multi-items scales. For this study, organizational performance and knowledge management effectiveness were taken as dependents variables. Leadership styles of transformational and transactional and organizational culture were taken as independent variables. Organizational culture (mission, consistency, adaptability, and involvement) served as mediator variable. A questionnaire was used to collect data; this questionnaire was administered to 251 faculty and administrative leaders employed at 20 universities and colleges across the United States of America. Only 136 were entirely completed and deemed useful for the study. Structural equation modeling and Confirmatory Factor Analysis within SEM were adopted for data analysis. Results were presented using frequency distribution tables and graphs. Key findings suggested that organizational culture and transformational leadership impacted knowledge management effectiveness. But transactional leadership did not. Consequently, knowledge management effectiveness impacted organizational performance. While organizational culture mediated the effects of transformational leadership on knowledge management effectiveness, no mediating effect of organizational culture was found on the effect of transactional leadership on knowledge management effectiveness. Organizational culture has the largest positive impact on knowledge management effectiveness. These results may inform the successful implementation of KM practices, which in term improve the performance of higher educational institutions across the United States of America

Cross-Lingual Taxonomy Alignment with Bilingual Biterm Topic Model

As more and more multilingual knowledge becomes available on the Web, knowledge sharing across languages has become an important task to benefit many applications. One of the most crucial kinds of knowledge on the Web is taxonomy, which is used to organize and classify the Web data. To facilitate knowledge sharing across languages, we need to deal with the problem of cross-lingual taxonomy alignment, which discovers the most relevant category in the target taxonomy of one language for each category in the source taxonomy of another language. Current approaches for aligning cross-lingual taxonomies strongly rely on domain-specific information and the features based on string similarities. In this paper, we present a new approach to deal with the problem of cross-lingual taxonomy alignment without using any domain-specific information. We first identify the candidate matched categories in the target taxonomy for each category in the source taxonomy using the cross-lingual string similarity. We then propose a novel bilingual topic model, called Bilingual Biterm Topic Model (BiBTM), to perform exact matching. BiBTM is trained by the textual contexts extracted from the Web. We conduct experiments on two kinds of real world datasets. The experimental results show that our approach significantly outperforms the designed state-of-the-art comparison methods

The Palgrave Handbook of Digital Russia Studies

This open access handbook presents a multidisciplinary and multifaceted perspective on how the ‘digital’ is simultaneously changing Russia and the research methods scholars use to study Russia. It provides a critical update on how Russian society, politics, economy, and culture are reconfigured in the context of ubiquitous connectivity and accounts for the political and societal responses to digitalization. In addition, it answers practical and methodological questions in handling Russian data and a wide array of digital methods. The volume makes a timely intervention in our understanding of the changing field of Russian Studies and is an essential guide for scholars, advanced undergraduate and graduate students studying Russia today

The Palgrave Handbook of Digital Russia Studies

This open access handbook presents a multidisciplinary and multifaceted perspective on how the ‘digital’ is simultaneously changing Russia and the research methods scholars use to study Russia. It provides a critical update on how Russian society, politics, economy, and culture are reconfigured in the context of ubiquitous connectivity and accounts for the political and societal responses to digitalization. In addition, it answers practical and methodological questions in handling Russian data and a wide array of digital methods. The volume makes a timely intervention in our understanding of the changing field of Russian Studies and is an essential guide for scholars, advanced undergraduate and graduate students studying Russia today