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Label definitions augmented interaction model for legal charge prediction
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In: https://link.springer.com/book/10.1007/978-3-030-72113-8 (2021)
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DNA Methylation Biomarkers of IQ Reduction are Associated with Long-term Lead Exposure in School Aged Children in Southern China
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The trans-ancestral genomic architecture of glycemic traits.
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The trans-ancestral genomic architecture of glycemic traits.
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In: Nature genetics, vol. 53, no. 6, pp. 840-860 (2021)
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Are the protective benefits of vitamin D in neurodegenerative disease dependent on route of administration? A systematic review
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In: https://www-tandfonline-com.proxy.library.adelaide.edu.au/doi/full/10.1080/1028415X.2018.1493807 (2020)
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Prescribing competency assessment for Canadian medical students: a pilot evaluation
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Native-speakerism in ELT: A case study of English language education in China
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The Next Frontier in Communication and the ECLIPPSE Study: Bridging the Linguistic Divide in Secure Messaging
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In: Schillinger, D; McNamara, D; Crossley, S; Lyles, C; Moffet, HH; Sarkar, U; et al.(2017). The Next Frontier in Communication and the ECLIPPSE Study: Bridging the Linguistic Divide in Secure Messaging. JOURNAL OF DIABETES RESEARCH. doi:10.1155/2017/1348242. UC San Francisco: Retrieved from: http://www.escholarship.org/uc/item/0384j5vf (2017)
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A comparative study of students' strategy use in reading texts for the IELTS test and those for academic study
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Evaluation of statistical text normalisation techniques for Twitter
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Abstract:
One of the major challenges in the era of big data use is how to ‘clean’ the vast amount of data, particularly from micro-blog websites like Twitter. Twitter messages, called tweets, are commonly written in ill-forms, including abbreviations, repeated characters, and misspelled words. These ‘noisy tweets’ require text normalisation techniques to detect and convert them into more accurate English sentences. There are several existing techniques proposed to solve these issues, however each technique possess some limitations and therefore cannot achieve good overall results. This paper aims to evaluate individual existing statistical normalisation methods and their possible combinations in order to find the best combination that can efficiently clean noisy tweets at the character-level, which contains abbreviations, repeated letters and misspelled words. Tested on our Twitter sample dataset, the best combination can achieve 88% accuracy in the Bilingual Evaluation Understudy (BLEU) score and 7% Word Error Rate (WER) score, both of which are considered better than the baseline model.
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Keyword:
080109 Pattern Recognition and Data Mining; 150502 Marketing Communications; big data; data normalisation; lexical normalisation; micro-blogs; noisy tweets; normalisation; social media; statistical language models; text cleansers; tweets; Twitter
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URL: https://hdl.handle.net/10652/3808
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Evaluation of statistical text normalisation techniques for Twitter
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Evaluation of statistical text normalisation techniques for Twitter
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How does studying abroad change Chinese students' choice of reading strategies?
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Liu, J.. - : University of Toronto Press, 2016
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Methods for conducting systematic reviews of risk factors in low- and middle-income countries
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Social touch gesture recognition using random forest and boosting on distinct feature sets
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Reading transition in Chinese international students: through the lens of activity system theory
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Physical activity during pregnancy and language development in the offspring
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A statistical study on ELF-whistlers/emissions and M ≥ 5.0 earthquakes in Taiwan
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