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Hands-on natural language processing with PyTorch 1.x : build smart, AI-driven linguistic applications using deep learning and NLP techniques
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BLLDB
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UB Frankfurt Linguistik
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Determining Tone of a Body of Text
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In: Senior Projects Spring 2020 (2020)
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BASE
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Modelling source- and target-language syntactic Information as conditional context in interactive neural machine translation
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In: Gupta, Kamal Kumar, Haque, Rejwanul orcid:0000-0003-1680-0099 , Ekbal, Asif, Bhattacharyya, Pushpak and Way, Andy orcid:0000-0001-5736-5930 (2020) Modelling source- and target-language syntactic Information as conditional context in interactive neural machine translation. In: Proceedings of the 22nd Annual Conference of the European Association for Machine Translation, 2-6 Nov 2020, Lisboa, Portugal. (2020)
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BASE
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AlphaMWE: construction of multilingual parallel corpora with MWE annotations
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In: Han, Lifeng orcid:0000-0002-3221-2185 , Jones, Gareth J.F. orcid:0000-0003-2923-8365 and Smeaton, Alan F. orcid:0000-0003-1028-8389 (2020) AlphaMWE: construction of multilingual parallel corpora with MWE annotations. In: Joint Workshop on Multiword Expressions and Electronic Lexicons (MWE-LEX 2020), 13 Dec 2020, Barcelona, Spain (Online). (2020)
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BASE
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DIACR-Ita @ EVALITA2020: overview of the EVALITA2020 DiachronicLexical semantics (DIACR-Ita) task
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In: Basile, Pierpaolo, Caputo, Annalina orcid:0000-0002-7144-8545 , Caselli, Tommaso orcid:0000-0003-2936-0256 , Cassotti, Pierluigi and Varvara, Rossella orcid:0000-0001-9957-2807 (2020) DIACR-Ita @ EVALITA2020: overview of the EVALITA2020 DiachronicLexical semantics (DIACR-Ita) task. In: Seventh Evaluation Campaign of Natural Language Processing and Speech Tools for Italian, 17 Dec 2020, Online. (2020)
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BASE
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On the differences between human translations
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In: Popović, Maja orcid:0000-0001-8234-8745 (2020) On the differences between human translations. In: 22nd Annual Conference of the European Association for Machine Translation (EAMT 2020), 3 -5 Nov 2020, Lisbon, Portugal (Online). (2020)
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BASE
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A diachronic Italian corpus based on “L’Unit`a”
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In: Basile, Pierpaolo, Caputo, Annalina orcid:0000-0002-7144-8545 , Caselli, Tommaso orcid:0000-0003-2936-0256 , Cassotti, Pierluigi and Varvara, Rossella orcid:0000-0001-9957-2807 (2020) A diachronic Italian corpus based on “L’Unit`a”. In: Seventh Italian Conference on Computational Linguistics, 1-3 Mar 2021, Bologna (Online). (2020)
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BASE
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Neural machine translation between similar south-Slavic languages
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In: Popović, Maja orcid:0000-0001-8234-8745 and Poncelas, Alberto orcid:0000-0002-5089-1687 (2020) Neural machine translation between similar south-Slavic languages. In: 2020 Fifth Conference on Machine Translation (WMT20), 19-20 Nov 2020, Dominican Republic (Online). (2020)
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BASE
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Annotating verbal MWEs in Irish for the PARSEME Shared Task 1.2
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In: Walsh, Abigail, Lynn, Teresa and Foster, Jennifer orcid:0000-0002-7789-4853 (2020) Annotating verbal MWEs in Irish for the PARSEME Shared Task 1.2. In: Joint Workshop on Multiword Expressions and Electronic Lexicons, 13 Dec 2020, Barcelona, Spain (Online). (2020)
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BASE
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GM-CTSC at SemEval-2020 Task 1: Gaussian mixtures cross temporal similarity clustering
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In: Cassotti, Pierluigi, Caputo, Annalina orcid:0000-0002-7144-8545 , Polignano, Marco orcid:0000-0002-3939-0136 and Basile, Pierpaolo orcid:0000-0002-0545-1105 (2020) GM-CTSC at SemEval-2020 Task 1: Gaussian mixtures cross temporal similarity clustering. In: Fourteenth Workshop on Semantic Evaluation, Dec 2020, Barcelona (Online). (2020)
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Abstract:
This paper describes the system proposed by the Random team for SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection. We focus our approach on the detection problem. Given the semantics of words captured by temporal word embeddings in different time periods, we investigate the use of unsupervised methods to detect when the target word has gained or lost senses. To this end, we define a new algorithm based on Gaussian Mixture Models to cluster the target similarities computed over the two periods. We compare the proposed approach with a number of similarity-based thresholds. We found that, although the performance of the detection methods varies across the word embedding algorithms, the combination of Gaussian Mixture with Temporal Referencing resulted in our best system.
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Keyword:
Computational linguistics
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URL: http://doras.dcu.ie/25945/
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BASE
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Syntax-informed interactive neural machine translation
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In: Gupta, Kamal Kumar, Haque, Rejwanul orcid:0000-0003-1680-0099 , Ekbal, Asif, Bhattacharyya, Pushpak and Way, Andy orcid:0000-0001-5736-5930 (2020) Syntax-informed interactive neural machine translation. In: The International Joint Conference on Neural Networks (IJCNN), 19-24 July 2020, Glasgow, UK (Online). (2020)
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BASE
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Bilingual lexicon induction across orthographically-distinct under-resourced Dravidian languages
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In: Chakravarthi, Bharathi Raja orcid:0000-0002-4575-7934 , Rajasekaran, Navaneethan, Arcan, Mihael orcid:0000-0002-3116-621X , McGuinness, Kevin orcid:0000-0003-1336-6477 , O'Connor, Noel E. orcid:0000-0002-4033-9135 and McCrae, John P. orcid:0000-0002-7227-1331 (2020) Bilingual lexicon induction across orthographically-distinct under-resourced Dravidian languages. In: 7th Workshop on NLP for Similar Languages, Varieties and Dialects, 13 Dec 2020, Barcelona, Spain (Online). (2020)
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BASE
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