NextGen AML: Distributed Deep Learning based Language Technologies to Augment Anti Money Laundering Investigation

no code implementations • ACL 2018 • Jingguang Han, Utsab Barman, Jeremiah Hayes, Jinhua Du, Edward Burgin, Dadong Wan

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.

Decision Making Entity Linking +2

Part-of-speech Tagging of Code-mixed Social Media Content: Pipeline, Stacking and Joint Modelling

no code implementations • WS 2016 • Utsab Barman, Joachim Wagner, Jennifer Foster

Language Identification Part-Of-Speech Tagging

DCU-UVT: Word-Level Language Classification with Code-Mixed Data

no code implementations • WS 2014 • Utsab Barman, Joachim Wagner, Grzegorz Chrupa{\l}a, Jennifer Foster

Classification General Classification +1

Code Mixing: A Challenge for Language Identification in the Language of Social Media

no code implementations • WS 2014 • Utsab Barman, Amitava Das, Joachim Wagner, Jennifer Foster

Language Identification

DCU: Aspect-based Polarity Classification for SemEval Task 4

no code implementations • SEMEVAL 2014 • Joachim Wagner, Piyush Arora, Santiago Cortes, Utsab Barman, Dasha Bogdanova, Jennifer Foster, Lamia Tounsi

Aspect-Based Sentiment Analysis (ABSA) Classification +1