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Automatic processing of code-mixed social media content
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Barman, Utsab. - : Dublin City University. School of Computing, 2019. : Dublin City University. ADAPT, 2019
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In: Barman, Utsab (2019) Automatic processing of code-mixed social media content. PhD thesis, Dublin City University. (2019)
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NextGen AML: distributed deep learning based language technologies to augment anti money laundering Investigation
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In: Han, Jingguang, Barman, Utsab, Hayes, Jer, Du, Jinhua orcid:0000-0002-3267-4881 , Burgin, Edward and Wan, Dadong (2018) NextGen AML: distributed deep learning based language technologies to augment anti money laundering Investigation. In: 56th Annual Meeting of the Association for Computational Linguistics-System Demonstrations, 15-20 July 201, Melbourne, Australia. (2018)
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Part-of-speech tagging of code-mixed social media content: pipeline, stacking and joint modelling
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In: Barman, Utsab, Wagner, Joachim orcid:0000-0002-8290-3849 and Foster, Jennifer orcid:0000-0002-7789-4853 (2016) Part-of-speech tagging of code-mixed social media content: pipeline, stacking and joint modelling. In: Second Workshop on Computational Approaches to Code Switching, 2 Nov 2016, Austin, Texas, USA. (2016)
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Code mixing: a challenge for language identification in the language of social media
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In: Barman, Utsab, Das, Amitava orcid:0000-0003-3418-463X , Wagner, Joachim orcid:0000-0002-8290-3849 and Foster, Jennifer orcid:0000-0002-7789-4853 (2014) Code mixing: a challenge for language identification in the language of social media. In: First Workshop on Computational Approaches to Code Switching, 25 Oct 2014, Doha, Qatar. (2014)
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DCU: aspect-based polarity classification for SemEval task 4
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In: Wagner, Joachim orcid:0000-0002-8290-3849 , Arora, Piyush orcid:0000-0002-4261-2860 , Cortes, Santiago, Barman, Utsab, Bogdanova, Dasha, Foster, Jennifer orcid:0000-0002-7789-4853 and Tounsi, Lamia (2014) DCU: aspect-based polarity classification for SemEval task 4. In: International Workshop on Semantic Evaluation (SemEval-2014), 23-24 Aug 2014, Dublin, Ireland. ISBN 978-1-941643-24-2 (2014)
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Abstract:
We describe the work carried out by DCU on the Aspect Based Sentiment Analysis task at SemEval 2014. Our team submitted one constrained run for the restaurant domain and one for the laptop domain for sub-task B (aspect term polarity prediction), ranking highest out of 36 systems on the restaurant test set and joint highest out of 32 systems on the laptop test set.
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Keyword:
Attitude: Polarity; Computational linguistics; Consumer behaviour; Opinion mining; Sentiment analysis
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URL: http://doras.dcu.ie/20324/
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