Výsledky vyhledávání - "Utsab Barman"

NextGen AML: distributed deep learning based language technologies to augment anti money laundering Investigation

Autor: Edward Burgin, Dadong Wan, Jingguang Han, Jeremiah Hayes, Utsab Barman, Jinhua Du

Publikováno v: 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.
ACL (4)

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 thos

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b25497eb7f529bb4c10f68bf3866b0de
http://doras.dcu.ie/23358/

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Part-of-speech tagging of code-mixed social media content: pipeline, stacking and joint modelling

Autor: Jennifer Foster, Utsab Barman, Joachim Wagner

Publikováno v: 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.
CodeSwitch@EMNLP

Multilingual users of social media sometimes use multiple languages during conversation. Mixing multiple languages in content is known as code-mixing. We annotate a subset of a trilingual code-mixed corpus (Barman et al., 2014) with part-of-speech (P

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5c86c311befe7f891033ad572a66b3a0
http://doras.dcu.ie/23067/

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

Autor: Joachim Wagner, Amitava Das, Jennifer Foster, Utsab Barman

Publikováno v: 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.
CodeSwitch@EMNLP

In social media communication, multilingual speakers often switch between languages, and, in such an environment, automatic language identification becomes both a necessary and challenging task. In this paper, we describe our work in progress on the

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5f796bcc43a6a3fa05b2a5963b576234

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

Autor: Jennifer Foster, Joachim Wagner, Grzegorz Chrupała, Utsab Barman

Publikováno v: First Workshop on Computational Approaches to Code Switching, 127-132
STARTPAGE=127;ENDPAGE=132;TITLE=First Workshop on Computational Approaches to Code Switching
CodeSwitch@EMNLP
Barman, Utsab, Wagner, Joachim ORCID: 0000-0002-8290-3849 , Chrupała, Grzegorz ORCID: 0000-0001-9498-6912 and Foster, Jennifer ORCID: 0000-0002-7789-4853 (2014) DCU-UVT: Word-level language classification with code-mixed data. In: First Workshop on Computational Approaches to Code Switching, 25 Oct 2014, Doha, Qatar.

This paper describes the DCU-UVT team’s participation in the Language Identification in Code-Switched Data shared task in the Workshop on Computational Approaches to Code Switching. Word-level classification experiments were carried out using a sim

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::58fbb0db6ce76312dfa62ef6fef78d64
https://research.tilburguniversity.edu/en/publications/5381e879-7e60-480e-8141-1376ef32a5c6

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A Statistics-Based Semantic Textual Entailment System

Autor: Partha Pakray, Alexander Gelbukh, Sivaji Bandyopadhyay, Utsab Barman

Publikováno v: Advances in Artificial Intelligence ISBN: 9783642253232
MICAI (1)
ResearcherID

We present a Textual Entailment (TE) recognition system that uses semantic features based on the Universal Networking Language (UNL). The proposed TE system compares the UNL relations in both the text and the hypothesis to arrive at the two-way entai

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1be11bf73bed4354db3579da10bdcd29
https://doi.org/10.1007/978-3-642-25324-9_23

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