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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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Abstract:
Most of the current anti money laundering (AML) systems, using handcrafted rules, are heavily reliant on existing structured databases, which are not capable of effectively and efficiently identifying hidden and complex ML activities, especially those with dynamic and timevarying characteristics, resulting in a high percentage of false positives. Therefore, analysts1 are engaged for further investigation which significantly increases human capital cost and processing time. To alleviate these issues, this paper presents a novel framework for the next generation AML by applying and visualizing deep learning-driven natural language processing (NLP) technologies in a distributed and scalable manner to augment AML monitoring and investigation. The proposed distributed framework performs news and tweet sentiment analysis, entity recognition, relation extraction, entity linking and link analysis on different data sources (e.g. news articles and tweets) to provide additional evidence to human investigators for final decisionmaking. Each NLP module is evaluated on a task-specific data set, and the overall experiments are performed on synthetic and real-world datasets. Feedback from AML practitioners suggests that our system can reduce approximately 30% time and cost compared to their previous manual approaches of AML investigation.
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Keyword:
Machine translating
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URL: http://doras.dcu.ie/23358/
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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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