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21
Improving the Performance of Vietnamese&ndash ; Korean Neural Machine Translation with Contextual Embedding
In: Applied Sciences ; Volume 11 ; Issue 23 (2021)
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22
Collecting and annotating corpora for three under-resourced languages of France: Methodological issues
Bernhard, Delphine; Ligozat, Anne-Laure; Bras, Myriam. - : University of Hawaii Press, 2021
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23
An evaluation of part-of-speech taggers for French
Mattiuzzi, Silvia. - : Université de Genève, 2021
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24
Collecting and annotating corpora for three under-resourced languages of France: Methodological issues
Bernhard, Delphine; Ligozat, Anne-Laure; Bras, Myriam. - : University of Hawaii Press, 2021
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25
The impact of Arabic part of speech tagging on sentiment analysis: A new corpus and deep learning approach
In: Test Series for Scopus Harvesting 2021 (2021)
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26
Testing for Grammatical Category Abstraction in Neural Language Models
In: Proceedings of the Society for Computation in Linguistics (2021)
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27
Measuring grammatical status in Chinese through quantitative corpus analysis
In: Corpora. - Edinburgh : Univ. Press 15 (2020) 3, 317-342
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28
Crowdsourcing linguistic resources for natural non-standardised languages processing ; Myriadisation de ressources linguistiques pour le traitement automatique de langues non standardisées
Millour, Alice. - : HAL CCSD, 2020
In: https://hal.archives-ouvertes.fr/tel-03083213 ; Informatique et langage [cs.CL]. Sorbonne Universite, 2020. Français (2020)
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29
Hierarchical-Task Reservoir for Anytime POS Tagging from Continuous Speech
In: 2020 International Joint Conference on Neural Networks (IJCNN 2020) ; https://hal.inria.fr/hal-02594495 ; 2020 International Joint Conference on Neural Networks (IJCNN 2020), Jul 2020, Glasgow, Scotland, United Kingdom ; https://wcci2020.org/ (2020)
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30
Het transcriptieprotocol van het Gesproken Corpus van de Nederlandse Dialecten (GCND)
In: Belgien / Commission royale de toponymie et de dialectologie. Bulletin de la Commission Royale de Toponymie & Dialectologie. - Bruxelles 92 (2020), 83-115
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31
The CLASSLA-StanfordNLP model for morphosyntactic annotation of standard Macedonian 1.0
Ljubešić, Nikola; Zdravkova, Katerina; Stojanoska, Sanja. - : Jožef Stefan Institute, 2020
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32
The CLASSLA-StanfordNLP model for morphosyntactic annotation of standard Serbian 1.1
Ljubešić, Nikola. - : Jožef Stefan Institute, 2020
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33
Spoken Torlak dialect corpus 1.0 (transcription)
Vuković, Teodora. - : Slavisches Seminar, University of Zurich, 2020
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34
The CLASSLA-StanfordNLP model for morphosyntactic annotation of standard Croatian 1.1
Ljubešić, Nikola. - : Jožef Stefan Institute, 2020
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35
Annotated Corpus of Pre-Standardized Balkan Slavic Literature
Šimko, Ivan. - : Slavic Seminary, University of Zurich, 2020
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36
The CLASSLA-StanfordNLP model for morphosyntactic annotation of standard Bulgarian 1.0
Ljubešić, Nikola; Osenova, Petya; Simov, Kiril. - : Jožef Stefan Institute, 2020. : IICT-BAS, 2020
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37
The CLASSLA-StanfordNLP model for morphosyntactic annotation of non-standard Serbian 1.0
Ljubešić, Nikola; Štefanec, Vanja. - : Jožef Stefan Institute, 2020
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38
The CLASSLA-StanfordNLP model for morphosyntactic annotation of non-standard Croatian 1.0
Ljubešić, Nikola; Štefanec, Vanja. - : Jožef Stefan Institute, 2020
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39
The CLASSLA-StanfordNLP model for morphosyntactic annotation of non-standard Slovenian 1.0
Ljubešić, Nikola. - : Jožef Stefan Institute, 2020
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40
The CLASSLA-StanfordNLP model for morphosyntactic annotation of standard Slovenian 1.1
Ljubešić, Nikola. - : Jožef Stefan Institute, 2020
Abstract: This model for morphosyntactic annotation of standard Slovenian was built with the CLASSLA-StanfordNLP tool (https://github.com/clarinsi/classla-stanfordnlp) by training on the ssj500k training corpus (http://hdl.handle.net/11356/1210) and using the CLARIN.SI-embed.sl word embeddings (http://hdl.handle.net/11356/1204). The model produces simultaneously UPOS, FEATS and XPOS (MULTEXT-East) labels. The estimated F1 of the XPOS annotations is ~97.06. The difference to the previous version of the model is that now the whole XPOS tag is predicted and not specific characters, as was the case in stanfordnlp, which resulted in illegal XPOS tags (and slightly decreased performance).
Keyword: language model; part-of-speech tagging
URL: http://hdl.handle.net/11356/1312
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