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MM-COVID: A Multilingual and Multimodal Data Repository for Combating COVID-19 Disinformation ...
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MM-COVID: A Multilingual and Multimodal Data Repository for Combating COVID-19 Disinformation ...
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Text Adjuncts and Comprehension with University Level Second Language Readers ...
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Liu, Huan. - : Washington University in St. Louis, 2021
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Mitigating Bias in Session-based Cyberbullying Detection: A Non-Compromising Approach ...
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Learning to Selectively Learn for Weakly-supervised Paraphrase Generation ...
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"Let's Eat Grandma": When Punctuation Matters in Sentence Representation for Sentiment Analysis ...
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MM-COVID: A Multilingual and Multimodal Data Repository for Combating COVID-19 Disinformation ...
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Hierarchical Propagation Networks for Fake News Detection: Investigation and Exploitation
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In: Proceedings of the International AAAI Conference on Web and Social Media; Vol. 14 (2020): Fourteenth International AAAI Conference on Web and Social Media; 626-637 ; 2334-0770 ; 2162-3449 (2020)
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The Chinese Version of Rochester Participatory Decision-Making Scale (RPAD): Reliability and Validity
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In: Evid Based Complement Alternat Med (2020)
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Probabilistic Relational Supervised Topic Modelling using Word Embeddings
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Abstract:
The increasing pace of change in languages affects many applications and algorithms for text processing. Researchers in Natural Language Processing (NLP) have been striving for more generalized solutions that can cope with continuous change. This is even more challenging when applied on short text emanating from social media. Furthermore, increasingly social media have been casting a major influence on both the development and the use of language. Our work is motivated by the need to develop NLP techniques that can cope with short informal text as used in social media alongside the massive proliferation of textual data uploaded daily on social media. In this paper, we describe a novel approach for Short Text Topic Modelling using word embeddings and taking into account any informality of words in the social media text with the aim of addressing the challenge of reducing noise in messy text. We present a new algorithm derived from the Term Frequency -Inverse Document Frequency (TF-IDF), named Term Frequency - Inverse Context Term Frequency (TF-ICTF). TF-ICTF relies on a probabilistic relation between words and context with respect to time. Our experimental work shows promising results against other state-of-the-art methods.
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Keyword:
QA75 Electronic computers. Computer science
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URL: http://repository.essex.ac.uk/24228/ https://doi.org/10.1109/BigData.2018.8622326 http://repository.essex.ac.uk/24228/1/fasli_big_data_01.pdf
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Identifying Rhetorical Questions in Social Media
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In: Proceedings of the International AAAI Conference on Web and Social Media; Vol. 10 No. 1 (2016): Tenth International AAAI Conference on Web and Social Media ; 2334-0770 ; 2162-3449 (2016)
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A Study on College English Teaching System Characterized by Need-Oriented Cultivation and Personalized Development: Illustrated by the Reform Practice in China University of Petroleum
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In: Studies in Literature and Language; Vol 10, No 4 (2015): Studies in Literature and Language; 115-123 ; 1923-1563 ; 1923-1555 (2015)
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Invited Speaker Support for SBP Conference Series (SBP 2014) held in April, 2014 in Washington, DC.
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