Chapter published in:
Parallel Corpora for Contrastive and Translation Studies: New resources and applications
Edited by Irene Doval and M. Teresa Sánchez Nieto
[Studies in Corpus Linguistics 90] 2019
► pp. 281298
Beaufort, Richard, Roekhaut, Sophie, Cougnon, Louise-Amélie & Fairon, Cédrick
2010A hybrid rule/model-based finite-state framework for normalizing SMS Messages. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, 770–779.Google Scholar
Bérard, Alexandre, Servan, Christophe, Pietquin, Olivier & Besacier, Laurent
2016Multivec: A multilingual and multilevel representation learning toolkit for NLP. The 10th edition of the Language Resources and Evaluation Conference, 4188–4192.Google Scholar
Bird, Steven, Loper, Edward & Klein, Ewan
2009Natural Language Processing with Python. San Francisco CA: O’Reilly Media.Google Scholar
Bojanowski, Piotr, Grave, Edouard, Joulin, Armand & Mikolov, Tomas
2016Enriching Word Vectors with Subword Information. https://​arxiv​.org​/abs​/1607​.04606> (13 May 2017).Google Scholar
Choudhury, Monojit, Saraf, Rahul, Jain, Vijit, Sudeshna, Sarkar & Basu, Anupam
2007Investigation and modeling of the structure of texting language. International Journal on Document Analysis and Recognition 10(3): 157–174. CrossrefGoogle Scholar
De Clercq Orphée, Schulz, Sarah, Desmet, Bart, Lefever, Els, Hoste, Véronique
2013Normalization of Dutch user-generated content. Proceedings of 9th International Conference on Recent Advances in Natural Language Processing, 179–188. Berlin: Springer.Google Scholar
Fairon, Cécrick, Klein, Jean R. & Paumier, Sébastien
2007Le langage SMS: étude d'un corpus informatisé à partir de l'enquête ‘Faites don de vos SMS à la science’. Louvain-la-Neuve: Presses universitaires de Louvain.Google Scholar
Firth, John R.
1957A synopsis of linguistic theory 1930–1955. Studies in Linguistic Analysis, 1–32. Oxford: Blackwell.Google Scholar
Jurafsky, Daniel & Martin, James H.
2014Speech and Language Processing. Englewood Cliffs NJ: Prentice Hall.Google Scholar
Kobus, Catherine, Yvon, François & Damnati, Géraldine
2008Normalizing SMS: Are two metaphors better than one? Proceedings of the 22nd International Conference on Computational Linguistics 1, 441–448.Google Scholar
Koch, Peter & Oesterreicher, Wulf
2001Gesprochene und geschriebene Sprache. Französisch, Italienisch, Spanisch. Berlin: De Gruyter.Google Scholar
Koehn, Philipp, Hoang, Hieu, Birch, Alexandra, Callison-Burch, Chris, Federico, Marcello, Bertoldi, Brooke Cowan, Nicola, Shen, Wade, Moran, Christine, Zens, Richard, Dyer, Chris, Bojar, Ondřej, Constantin, Alexandra & Herbst, Evan
2007Moses: Open source toolkit for statistical machine translation. Proceedings of the 45th annual meeting of the ACL on interactive poster and demonstration sessions, 177–180. CrossrefGoogle Scholar
Li, Chen & Liu, Yang
2012Normalization of text messages using character-& phone-based machine translation approaches. Proceedings of 13th Annual Conference of the International Speech Communication Association, 2330–2333.Google Scholar
Mikolov, Tomas, Chen, Kai, Corrado, Greg & Dean, Jeffrey
2013aEfficient estimation of word representations in vector space. The Workshop Proceedings of the International Conference on Learning Representations. https://​arxiv​.org​/abs​/1301​.3781> (13 May 2017).Google Scholar
Mikolov, Thomas, Ilya, Sutskever, Chen, Kai, Corrado, Greg & Dean, Jeffrey
2013bDistributed representations of words and phrases and their compositionality. Advances in Neural Information Processing Systems.https://​arxiv​.org​/pdf​/1310​.4546​.pdf> (13 May 2017).Google Scholar
Och, Franz Josef & Ney, Hermann
2003A systematic comparison of various statistical alignment models. Computational Linguistics 29(1): 19–51. CrossrefGoogle Scholar
Pennell, Deana L. & Liu, Yang
2011A character-level machine translation approach for normalization of SMS Abbreviations. Proceedings of International Joint Conference on Natural Language Processing (IJCNLP): 974–982.Google Scholar
Rong, Xin
2014word2vec parameter learning explained. https://​arxiv​.org​/abs​/1411​.2738> (13 May 2017).Google Scholar
sms4science project
Sridhar, V. K. R.
2015Unsupervised text normalization using distributed representations of words and phrases. Proceedings of the 2015 Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (HLT-NAACL): 8–16.Google Scholar
Van Compernolle, Rémi A.
2010The (slightly more) productive use of ne in Montreal French chat. Language Sciences 32(4): 447–463. CrossrefGoogle Scholar
Yvon, François
2010Rewriting the orthography of SMS messages. Natural Language Engineering 16(2): 133–159. CrossrefGoogle Scholar