2,792 research outputs found

    State of the art review : language testing and assessment (part two).

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    In Part 1 of this two-part review article (Alderson & Banerjee, 2001), we first addressed issues of washback, ethics, politics and standards. After a discussion of trends in testing on a national level and in testing for specific purposes, we surveyed developments in computer-based testing and then finally examined self-assessment, alternative assessment and the assessment of young learners. In this second part, we begin by discussing recent theories of construct validity and the theories of language use that help define the constructs that we wish to measure through language tests. The main sections of the second part concentrate on summarising recent research into the constructs themselves, in turn addressing reading, listening, grammatical and lexical abilities, speaking and writing. Finally we discuss a number of outstanding issues in the field

    Virtual Reality Applications and Development

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    Virtual Reality (VR) has existed for many years; however, it has only recently gained wide spread popularity and commercial use. This change comes from the innovations in head mounted displays (HMDs) and from the work of many software engineers making quality user experiences (UX). In this thesis, four areas are explored inside of VR. One area of research is within the use of VR for virtual environments and fire simulations. The second area of research is within the use of VR for eye tracking and medical simulations. The third area of research is within multiplayer development for more immersive collaborative simulations. Finally, the fourth area of research is within the development of typing in 3D for virtual reality. Extending from this final area of research, this thesis details an application that details more practical and granular details about developing for VR and using the real-time development platform, Unity

    A hybrid approach for transliterated word-level language identification: CRF with post processing heuristics

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    © {Owner/Author | ACM} {Year}. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in FIRE '14 Proceedings of the Forum for Information Retrieval Evaluation, http://dx.doi.org/10.1145/2824864.2824876[EN] In this paper, we describe a hybrid approach for word-level language (WLL) identification of Bangla words written in Roman script and mixed with English words as part of our participation in the shared task on transliterated search at Forum for Information Retrieval Evaluation (FIRE) in 2014. A CRF based machine learning model and post-processing heuristics are employed for the WLL identification task. In addition to language identification, two transliteration systems were built to transliterate detected Bangla words written in Roman script into native Bangla script. The system demonstrated an overall token level language identification accuracy of 0.905. The token level Bangla and English language identification F-scores are 0.899, 0.920 respectively. The two transliteration systems achieved accuracies of 0.062 and 0.037. The word-level language identification system presented in this paper resulted in the best scores across almost all metrics among all the participating systems for the Bangla-English language pair.We acknowledge the support of the Department of Electronics and Information Technology (DeitY), Government of India, through the project “CLIA System Phase II”. The research work of the last author was carried out in the framework of WIQ-EI IRSES (Grant No. 269180) within the FP 7 Marie Curie, DIANA-APPLICATIONS (TIN2012-38603-C02-01) projects and the VLC/CAMPUS Microcluster on Multimodal Interaction in Intelligent Systems.Banerjee, S.; Kuila, A.; Roy, A.; Naskar, SK.; Rosso, P.; Bandyopadhyay, S. (2014). A hybrid approach for transliterated word-level language identification: CRF with post processing heuristics. En FIRE '14 Proceedings of the Forum for Information Retrieval Evaluation. ACM. 170-173. https://doi.org/10.1145/2824864.2824876S170173Y. Al-Onaizan and K. Knight. Named entity translation: Extended abstract. In HLT, pages 122--124. Singapore, 2002.P. J. Antony, V. P. Ajith, and K. P. Suman. Feature extraction based english to kannada transliteration. In In hird International conference on Semantic E-business and Enterprise Computing. SEEC 2010, 2010.P. J. Antony, V. P. Ajith, and K. P. Suman. Kernel method for english to kannada transliteration. In International conference on-Recent trends in Information, Telecommunication and computing. ITC2010, 2010.M. Arbabi, S. M. Fischthal, V. C. Cheng, and E. Bart. Algorithms for arabic name transliteration. In IBM Journal of Research and Development, page 183. TeX Users Group, 1994.S. Banerjee, S. Naskar, and S. Bandyopadhyay. Bengali named entity recognition using margin infused relaxed algorithm. In TSD, pages 125--132. Springer International Publishing, 2014.U. Barman, J. Wagner, G. Chrupala, and J. Foster. Identification of languages and encodings in a multilingual document. page 127. EMNLP, 2014.K. R. Beesley. Language identifier: A computer program for automatic natural-language identification of on-line text. pages 47--54. ATA, 1988.P. F. Brown, S. A. D. Pietra, V. J. D. Pietra, and R. L. Mercer. Mercer: The mathematics of statistical machine translation: parameter estimation. pages 263--311. Computational Linguistics, 1993.M. Carpuat. Mixed-language and code-switching in the canadian hansard. page 107. EMNLP, 2014.G. Chittaranjan, Y. Vyas, K. Bali, and M. Choudhury. Word-level language identification using crf: Code-switching shared task report of msr india system. pages 73--79. EMNLP, 2014.A. Das, A. Ekbal, T. Mandal, and S. Bandyopadhyay. English to hindi machine transliteration system at news. pages 80--83. Proceeding of the Named Entities Workshop ACL-IJCNLP, Singapore, 2009.A. Ekbal, S. Naskar, and S. Bandyopadhyay. A modified joint source channel model for transliteration. pages 191--198. COLING-ACL Australia, 2006.I. Goto, N. Kato, N. Uratani, and T. Ehara. Transliteration considering context information based on the maximum entropy method. pages 125--132. MT-Summit IX, New Orleans, USA, 2003.R. Haque, S. Dandapat, A. K. Srivastava, S. K. Naskar, and A. Way. English to hindi transliteration using context-informed pb-smt:the dcu system for news 2009. NEWS 2009, 2009.S. Y. Jung, S. Hong, and E. Paek. An english to korean transliteration model of extended markov window.S. Y. Jung, S. L. Hong, and E. Paek. An english to korean transliteration model of extended markov window. pages 383--389. COLING, 2000.B. J. Kang and K. S. Choi. Automatic transliteration and back-transliteration by decision tree learning. LERC, May 2000.B. King and S. Abney. Labeling the languages of words in mixed-language documents using weakly supervised methods. pages 1110--1119. NAACL-HLT, 2013.R. Kneser and H. Ney. Improved backing-off for m-gram language modeling. In ICASSP, pages 181--184. Detroit, MI, 1995.R. Kneser and H. Ney. SRILM-an extensible language modeling toolkit. In Intl. Conf. on Spoken Language Processing, pages 901--904, 2002.K. Knight and J. Graehl. Machine transliteration. in computational linguistics. pages 599--612, 1998.P. Koehn, H. Hoang, A. Birch, C. Callison-Burch, M. Federico, N. Bertoldi, B. Cowan, W. Shen, C. Moran, R. Zens, C. Dyer, O. Bojar, A. Constantin, and E. Herbst. Moses: open source toolkit for statistical machine translation. In ACL, pages 177--180, 2007.P. Koehn, F. J. Och, and D. Marcu. Statistical phrase-based translation. In HLT-NAACL, 2003.A. Kumaran and T. Kellner. A generic framework for machine transliteration. In 30th annual international ACM SIGIR conference on Research and development in information retrieval, pages 721--722. ACM, 2007.H. Li, Z. Min, and J. Su. 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    Typical and Dyslexic Development in Learning to Read Chinese

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    Uncovering the myth of learning to read Chinese characters: phonetic, semantic, and orthographic strategies used by Chinese as foreign language learners

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    Oral Session - 6A: Lexical modeling: no. 6A.3Chinese is considered to be one of the most challenging orthographies to be learned by non-native speakers, in particular, the character. Chinese character is the basic reading unit that converges sound, form and meaning. The predominant type of Chinese character is semantic-phonetic compound that is composed of phonetic and semantic radicals, giving the clues of the sound and meaning, respectively. Over the last two decades, psycholinguistic research has made significant progress in specifying the roles of phonetic and semantic radicals in character processing among native Chinese speakers …postprin

    (Dis)connections between specific language impairment and dyslexia in Chinese

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    Poster Session: no. 26P.40Specific language impairment (SLI) and dyslexia describe language-learning impairments that occur in the absence of a sensory, cognitive, or psychosocial impairment. SLI is primarily defined by an impairment in oral language, and dyslexia by a deficit in the reading of written words. SLI and dyslexia co-occur in school-age children learning English, with rates ranging from 17% to 75%. For children learning Chinese, SLI and dyslexia also co-occur. Wong et al. (2010) first reported on the presence of dyslexia in a clinical sample of 6- to 11-year-old school-age children with SLI. The study compared the reading-related cognitive skills of children with SLI and dyslexia (SLI-D) with 2 groups of children …postprin
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