Home Catalogue search

eng

Refine your search:

Search in the Catalogues and Directories






	Sort by
Simple Search

Hits 1 – 5 of 5

1	Automatic processing of code-mixed social media content
	Barman, Utsab. - : Dublin City University. School of Computing, 2019. : Dublin City University. ADAPT, 2019
	In: Barman, Utsab (2019) Automatic processing of code-mixed social media content. PhD thesis, Dublin City University. (2019)
	BASE
	Show details

2	NextGen AML: distributed deep learning based language technologies to augment anti money laundering Investigation
	Hayes, Jer; Wan, Dadong; Barman, Utsab...
	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)
	BASE
	Show details

3	Part-of-speech tagging of code-mixed social media content: pipeline, stacking and joint modelling
	Barman, Utsab; Foster, Jennifer; Wagner, Joachim
	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)
	BASE
	Show details

4	Code mixing: a challenge for language identification in the language of social media
	Barman, Utsab; Das, Amitava; Wagner, Joachim; Foster, Jennifer
	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)
	Abstract: 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 problem of automatic language identification for the language of social media. We describe a new dataset that we are in the process of creating, which contains Facebook posts and comments that exhibit code mixing between Bengali, English and Hindi. We also present some preliminary word-level language identification experiments using this dataset. Different techniques are employed, including a simple unsupervised dictionary-based approach, supervised word-level classification with and without contextual clues, and sequence labelling using Conditional Random Fields. We find that the dictionary-based approach is surpassed by supervised classification and sequence labelling, and that it is important to take contextual clues into consideration.
	Keyword: Artificial intelligence; code switching; Computational linguistics; language identification; Machine learning; natural language processing; social media
	URL: http://doras.dcu.ie/25186/
	BASE
	Hide details

5	DCU: aspect-based polarity classification for SemEval task 4
	Wagner, Joachim; Foster, Jennifer; Arora, Piyush...
	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)
	BASE
	Show details

© 2013 - 2024 Lin|gu|is|tik | Imprint | Privacy Policy | Datenschutzeinstellungen ändern