Code Mixed Cross Script Factoid Question Classification - A Deep Learning Approach

[EN] Before the advent of the Internet era, code-mixing was mainly used in the spoken form. However, with the recent popular informal networking platforms such as Facebook, Twitter, Instagram, etc., in social media, code-mixing is being used more and more in written form. User-generated social media content is becoming an increasingly important resource in applied linguistics. Recent trends in social media usage have led to a proliferation of studies on social media content. Multilingual social media users often write native language content in non-native script (cross-script). Recently Banerjee et al. [9] introduced the code-mixed cross-script question answering research problem and reported that the ever increasing social media content could serve as a potential digital resource for less-computerized languages to build question answering systems. Question classification is a core task in question answering in which questions are assigned a class or a number of classes which denote the expected answer type(s). In this research work, we address the question classification task as part of the code-mixed cross-script question answering research problem. We combine deep learning framework with feature engineering to address the question classification task and enhance the state-of-the-art question classification accuracy by over 4% for code-mixed cross-script questions.The work of the third author was partially supported by the SomEMBED TIN2015-71147-C2-1-P MINECO research project.Banerjee, S.; Kumar Naskar, S.; Rosso, P.; Bandyopadhyay, S. (2018). Code Mixed Cross Script Factoid Question Classification - A Deep Learning Approach. Journal of Intelligent & Fuzzy Systems. 34(5):2959-2969. https://doi.org/10.3233/JIFS-169481S2959296934

Modeling Classifier for Code Mixed Cross Script Questions.

ABSTRACT With a boom in the internet, the social media text had been increasing day by day and the user generated content (such as tweets and blogs) in Indian languages are written using Roman script due to various socio-cultural and technological reasons. A majority of these posts are multilingual in nature and many involve code mixing where lexical items and grammatical features from two languages appear in one sentence. Focusing on this current multilingual scenario, code-mixed cross-script (i.e., non-native script) data gives rise to a new problem and presents serious challenges to automatic Question Answering (QA) and for this question classification will be required which is an important step towards QA. This paper proposes an approach to handle cross script question classification as it is an important task of question analysis which detects the category of the question

MSIR@FIRE: A Comprehensive Report from 2013 to 2016

[EN] India is a nation of geographical and cultural diversity where over 1600 dialects are spoken by the people. With the technological advancement, penetration of the internet and cheaper access to mobile data, India has recently seen a sudden growth of internet users. These Indian internet users generate contents either in English or in other vernacular Indian languages. To develop technological solutions for the contents generated by the Indian users using the Indian languages, the Forum for Information Retrieval Evaluation (FIRE) was established and held for the first time in 2008. Although Indian languages are written using indigenous scripts, often websites and user-generated content (such as tweets and blogs) in these Indian languages are written using Roman script due to various socio-cultural and technological reasons. A challenge that search engines face while processing transliterated queries and documents is that of extensive spelling variation. MSIR track was first introduced in 2013 at FIRE and the aim of MSIR was to systematically formalize several research problems that one must solve to tackle the code mixing in Web search for users of many languages around the world, develop related data sets, test benches and most importantly, build a research community focusing on this important problem that has received very little attention. This document is a comprehensive report on the 4 years of MSIR track evaluated at FIRE between 2013 and 2016.Somnath Banerjee and Sudip Kumar Naskar are supported by Media Lab Asia, MeitY, Government of India, under the Visvesvaraya PhD Scheme for Electronics & IT. The work of Paolo Rosso was partially supported by the MISMIS research project PGC2018-096212-B-C31 funded by the Spanish MICINN.Banerjee, S.; Choudhury, M.; Chakma, K.; Kumar Naskar, S.; Das, A.; Bandyopadhyay, S.; Rosso, P. (2020). MSIR@FIRE: A Comprehensive Report from 2013 to 2016. 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In: Proceedings of the forum for information retrieval evaluation, ACM, 2014. pp. 80–85.Gella S, Sharma J, Bali K. Query word labeling and back transliteration for Indian languages: shared task system description. FIRE Working Notes. 2013;3.Gupta DK, Kumar S, Ekbal A. Machine learning approach for language identification and transliteration. In: Proceedings of the forum for information retrieval evaluation, ACM, 2014. pp. 60–64.Gupta P, Bali K, Banchs RE, Choudhury M, Rosso P. Query expansion for mixed-script information retrieval. In: Proceedings of the 37th international ACM SIGIR conference on research and development in information retrieval, ACM, 2014. pp. 677–686.Gupta P, Rosso P, Banchs RE. Encoding transliteration variation through dimensionality reduction: fire shared task on transliterated search. In: Fifth forum for information retrieval evaluation. 2013.HB Barathi Ganesh, M Anand Kumar, KP Soman. Distributional semantic representation for information retrieval. 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Testing the Unfolding Theory of Turnover: Development of an Exit Survey

Understanding turnover has always been a concern for organizations. The costs of turnover to an organization are both direct and indirect, through financial and personnel consequences. By understanding why employees leave, organizations can create more effective retention strategies in hopes of keeping top talent. One theory of turnover, The Unfolding Theory, proposes that employees follow one of five cognitive pathways when deciding to leave an organization. Previous studies evaluating this theory have both methodological and administrative flaws, such as conducting interviews after employees leave the organization. The present study examined the Unfolding Theory by creating an exit survey based on all the aspects of the theory and administering the survey to 107 employees before leaving an organization. The results indicated that the Unfolding Theory does well to capture the processes employees engage in when deciding to leave an organization. However, the data also suggested that additional cognitive pathways may exist and that different groups of employees may have a higher prevalence for a particular pathway. The study discusses how organizations can utilize the findings to gather exit data more accurately, which will help to better understand why employees leave an organization

Staging Transformations for Multimodal Web Interaction Management

Multimodal interfaces are becoming increasingly ubiquitous with the advent of mobile devices, accessibility considerations, and novel software technologies that combine diverse interaction media. In addition to improving access and delivery capabilities, such interfaces enable flexible and personalized dialogs with websites, much like a conversation between humans. In this paper, we present a software framework for multimodal web interaction management that supports mixed-initiative dialogs between users and websites. A mixed-initiative dialog is one where the user and the website take turns changing the flow of interaction. The framework supports the functional specification and realization of such dialogs using staging transformations -- a theory for representing and reasoning about dialogs based on partial input. It supports multiple interaction interfaces, and offers sessioning, caching, and co-ordination functions through the use of an interaction manager. Two case studies are presented to illustrate the promise of this approach.Comment: Describes framework and software architecture for multimodal web interaction managemen

Childhood mental health: an ecological analysis of the effects of neighborhood characteristics

Research on childhood mental illness traditionally examines risk factors most proximal to the child. However, current trends reflect growing interest in how broader contextual factors contribute to psychopathology risk. In this study, we examined neighborhood‐level indicators as potential sources of chronic strain in a sample of 156 mother–child dyads; children were 8 to 12 years old. For most neighborhood indicators, data were collected at the level of census tracts using publicly available data sets. We hypothesized that these indicators would be both associated with greater overall mental health symptoms and specifically predictive of childhood symptoms of depression. We also examined potential mediators (maternal functioning and family cohesion) and moderators (maternal depression). Neighborhood indicators correlated with parents’ ratings of children's overall mental health problems, but did not correlate with children's self‐report of depression symptoms. Maternal functioning mediated neighborhood effects on children's overall mental health problems. Implications and directions for future research are presented.The current work was supported by the following grants from the National Institutes of Health, National Institute of Mental Health MH066077, MH082861, PI: Martha C. Tompson, Ph.D. and MH082861S1, PI: Gail N. Kemp, M.A., M.P.H. (MH066077 - National Institutes of Health, National Institute of Mental Health; MH082861 - National Institutes of Health, National Institute of Mental Health; MH082861S1 - National Institutes of Health, National Institute of Mental Health)Published versio