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DravidianCodeMix: Sentiment Analysis and Offensive Language Identification Dataset for Dravidian Languages in Code-Mixed Text ...
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DravidianCodeMix: Sentiment Analysis and Offensive Language Identification Dataset for Dravidian Languages in Code-Mixed Text ...
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DravidianCodeMix: Sentiment Analysis and Offensive Language Identification Dataset for Dravidian Languages in Code-Mixed Text ...
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4
Attentive fine-tuning of Transformers for Translation of low-resourced languages @LoResMT 2021 ...
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5
Hypers at ComMA@ICON: Modelling Aggressiveness, Gender Bias and Communal Bias Identification ...
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Dataset for Identification of Homophobia and Transophobia in Multilingual YouTube Comments ...
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https://www.aclweb.org/anthology/W19-6809/ ...
Chakravarthi, Bharathi Raja. - : Zenodo, 2021
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https://www.aclweb.org/anthology/W19-6809/ ...
Chakravarthi, Bharathi Raja. - : Zenodo, 2021
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9
Bilingual lexicon induction across orthographically-distinct under-resourced Dravidian languages
In: Chakravarthi, Bharathi Raja orcid:0000-0002-4575-7934 , Rajasekaran, Navaneethan, Arcan, Mihael orcid:0000-0002-3116-621X , McGuinness, Kevin orcid:0000-0003-1336-6477 , O'Connor, Noel E. orcid:0000-0002-4033-9135 and McCrae, John P. orcid:0000-0002-7227-1331 (2020) Bilingual lexicon induction across orthographically-distinct under-resourced Dravidian languages. In: 7th Workshop on NLP for Similar Languages, Varieties and Dialects, 13 Dec 2020, Barcelona, Spain (Online). (2020)
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10
A sentiment analysis dataset for code-mixed Malayalam-English
Sherly, Elizabeth; Jose, Navya; McCrae, John P.. - : European Language Resources Association (ELRA), 2020
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11
Corpus creation for sentiment analysis in code-mixed Tamil-English text
McCrae, John P.; Priyadharshini, Ruba; Muralidaran, Vigneshwaran. - : European Language Resources Association (ELRA), 2020
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12
Leveraging orthographic information to improve machine translation of under-resourced languages
Asoka Chakravarthi, Bharathi Raja. - : NUI Galway, 2020
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13
A comparative study of different state-of-the-art hate speech detection methods in Hindi-English code-mixed data
Fransen, Theodorus; McCrae, John P.; Chakravarthi, Bharathi Raja. - : European Language Resources Association (ELRA), 2020
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14
A survey of current datasets for code-switching research
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15
NUIG-Panlingua-KMI Hindi-Marathi MT Systems for Similar Language Translation Task @ WMT 2020
Chakravarthi, Bharathi Raja; Kumar, Ritesh; Rani, Priya. - : Association for Computational Linguistics, 2020
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16
A Sentiment Analysis Dataset for Code-Mixed Malayalam-English ...
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17
ULD@NUIG at SemEval-2020 Task 9: Generative Morphemes with an Attention Model for Sentiment Analysis in Code-Mixed Text ...
Abstract: Code mixing is a common phenomena in multilingual societies where people switch from one language to another for various reasons. Recent advances in public communication over different social media sites have led to an increase in the frequency of code-mixed usage in written language. In this paper, we present the Generative Morphemes with Attention (GenMA) Model sentiment analysis system contributed to SemEval 2020 Task 9 SentiMix. The system aims to predict the sentiments of the given English-Hindi code-mixed tweets without using word-level language tags instead inferring this automatically using a morphological model. The system is based on a novel deep neural network (DNN) architecture, which has outperformed the baseline F1-score on the test data-set as well as the validation data-set. Our results can be found under the user name "koustava" on the "Sentimix Hindi English" page ... : To be published in 14th International Workshop on Semantic Evaluation SemEval-2020 ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences
URL: https://dx.doi.org/10.48550/arxiv.2008.01545
https://arxiv.org/abs/2008.01545
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A Sentiment Analysis Dataset for Code-Mixed Malayalam-English ...
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A Sentiment Analysis Dataset for Code-Mixed Malayalam-English ...
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Aspects of Terminological and Named Entity Knowledge within Rule-Based Machine Translation Models for Under-Resourced Neural Machine Translation Scenarios ...
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