Utsab Barman


NextGen AML: Distributed Deep Learning based Language Technologies to Augment Anti Money Laundering Investigation
Jingguang Han | Utsab Barman | Jeremiah Hayes | Jinhua Du | Edward Burgin | Dadong Wan
Proceedings of ACL 2018, System Demonstrations

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 time-varying characteristics, resulting in a high percentage of false positives. Therefore, analysts 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 decision-making. 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.


Part-of-speech Tagging of Code-mixed Social Media Content: Pipeline, Stacking and Joint Modelling
Utsab Barman | Joachim Wagner | Jennifer Foster
Proceedings of the Second Workshop on Computational Approaches to Code Switching


DCU: Aspect-based Polarity Classification for SemEval Task 4
Joachim Wagner | Piyush Arora | Santiago Cortes | Utsab Barman | Dasha Bogdanova | Jennifer Foster | Lamia Tounsi
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

pdf bib
Code Mixing: A Challenge for Language Identification in the Language of Social Media
Utsab Barman | Amitava Das | Joachim Wagner | Jennifer Foster
Proceedings of the First Workshop on Computational Approaches to Code Switching

DCU-UVT: Word-Level Language Classification with Code-Mixed Data
Utsab Barman | Joachim Wagner | Grzegorz Chrupała | Jennifer Foster
Proceedings of the First Workshop on Computational Approaches to Code Switching