1,722,064 research outputs found
The CLASSLA-StanfordNLP model for morphosyntactic annotation of non-standard Croatian 1.0
No full textThis model for morphosyntactic annotation of non-standard Croatian was built with the CLASSLA-StanfordNLP tool (https://github.com/clarinsi/classla-stanfordnlp) by training on the hr500k training corpus (http://hdl.handle.net/11356/1210), the ReLDI-NormTagNER-hr corpus (http://hdl.handle.net/11356/1241), the RAPUT corpus (https://www.aclweb.org/anthology/L16-1513/) and the ReLDI-NormTagNER-sr corpus (http://hdl.handle.net/11356/1240), using the CLARIN.SI-embed.hr word embeddings (http://hdl.handle.net/11356/1205). These corpora were additionally augmented for handling missing diacritics by repeating parts of the corpora with diacritics removed. The model produces simultaneously UPOS, FEATS and XPOS (MULTEXT-East) labels. The estimated F1 of the XPOS annotations is ~95.11
The CLASSLA-StanfordNLP model for morphosyntactic annotation of standard Slovenian
No full textThe 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 ~96.7
The CLASSLA-Stanza model for semantic role labeling of standard Slovenian 2.0
No full textThe model for semantic role labeling of standard Slovenian was built with the CLASSLA-Stanza tool (https://github.com/clarinsi/classla) by training on the SUK training corpus (http://hdl.handle.net/11356/1747) and using the CLARIN.SI-embed.sl word embeddings (http://hdl.handle.net/11356/1204) extended with the MaCoCu-sl Slovenian web corpus (http://hdl.handle.net/11356/1517). The estimated F1 of the semantic role annotations is ~76.24.
The difference to the previous version of the model is that the model was trained using the SUK training corpus and the updated word embeddings
The CLASSLA-Stanza model for morphosyntactic annotation of non-standard Serbian 2.1
No full textThis model for morphosyntactic annotation of non-standard Serbian was built with the CLASSLA-Stanza tool (https://github.com/clarinsi/classla) by training on the SETimes.SR training corpus (http://hdl.handle.net/11356/1200), the ReLDI-NormTagNER-sr corpus (http://hdl.handle.net/11356/1794) and the hr500k training corpus (http://hdl.handle.net/11356/1792), using the CLARIN.SI-embed.sr word embeddings (http://hdl.handle.net/11356/1789). These corpora were additionally augmented for handling missing diacritics by repeating parts of the corpora with diacritics removed. The model produces simultaneously UPOS, FEATS and XPOS (MULTEXT-East) labels. The estimated F1 of the XPOS annotations is ~92.64.
The difference to the previous version of the model is that this version uses the new version of Serbian word embeddings and is trained on a combination of three training corpora (SETimes.SR, ReLDI-NormTagNER-sr, hr500k)
The CLASSLA-StanfordNLP model for UD dependency parsing of standard Croatian
No full textThe model for UD dependency parsing of standard Croatian was built with the CLASSLA-StanfordNLP tool (https://github.com/clarinsi/classla-stanfordnlp) by training on the UD-parsed portion of the hr500k training corpus (http://hdl.handle.net/11356/1183) and using the CLARIN.SI-embed.hr word embeddings (http://hdl.handle.net/11356/1205). The estimated LAS of the parser is ~85.9
Spoken corpus Gos VideoLectures 3.0 (audio)
No full textGos VideoLectures is an add-on to the Gos reference corpus of spoken Slovene (http://hdl.handle.net/11356/1040), and covers public academic speech. The Gos VideoLectures corpus contains a selection of public lectures available through the web portal Videolectures.net provided by the Jožef Stefan Institute, and covers 37 lectures with 16 hours of speech.
This resource contains only audio recordings of the corpus – annotated transcriptions are available at http://hdl.handle.net/11356/1190
Spoken corpus Gos VideoLectures 2.0 (audio)
No full textGos VideoLectures is an add-on to the Gos reference corpus of spoken Slovene (http://hdl.handle.net/11356/1040), and covers public academic speech. The Gos VideoLectures corpus contains a selection of public lectures available through the web portal Videolectures.net provided by the Jožef Stefan Institute, and covers 9.8 hours of speech.
This resource contains only audio recordings of the corpus – annotated transcriptions are available at http://hdl.handle.net/11356/1158
The CLASSLA-StanfordNLP model for UD dependency parsing of standard Slovenian
No full textThe model for UD dependency parsing of standard Slovenian was built with the CLASSLA-StanfordNLP tool (https://github.com/clarinsi/classla-stanfordnlp) by training on the UD-parsed portion of 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 estimated LAS of the parser is ~92.7
Monitor corpus of Slovene Trendi 2023-09
No full textThe Trendi corpus is a monitor corpus of Slovenian. It contains news articles from 106 media websites, published by 70 publishers. Trendi 2023-09 covers the period from January 2019 to September 2023, complementing the Gigafida 2.0 reference corpus of written Slovene (http://hdl.handle.net/11356/1320).
The contents of the Trendi corpus are obtained using the Jožef Stefan Institute Newsfeed service (http://newsfeed.ijs.si/). The texts have been annotated using the CLASSLA-Stanza pipeline (https://github.com/clarinsi/classla), including syntactic parsing according to the Universal Dependencies (https://universaldependencies.org/sl/) and Named Entities (https://nl.ijs.si/janes/wp-content/uploads/2017/09/SlovenianNER-eng-v1.1.pdf).
An important addition are topics or thematical categories, which have been automatically assigned to each text. There are 13 categories altogether: Arts and culture, Crime and accidents, Economy, Environment, Health, Leisure, Politics and Law, Science and Technology, Society, Sports, Weather, Entertainment, and Education. The text classification uses the following models: Text classification model SloBERTa-Trendi-Topics 1.0 (http://hdl.handle.net/11356/1709), Text classification model fastText-Trendi-Topics 1.0 (http://hdl.handle.net/11356/1710), and the SloBERTa model (https://huggingface.co/cjvt/sloberta-trendi-topics).
The corpus is currently not available as a downloadable dataset due to copyright restrictions but we hope to make at least some of it available in the near future. The corpus is accessible through CLARIN.SI concordancers.
As opposed to the previous version of the corpus, this version adds texts from March to September 2023, adds topic classification to files previous mistakenly without them, and corrects some other minor errors
The CLASSLA-Stanza model for UD dependency parsing of standard Slovenian 2.0
No full textThis model for UD dependency parsing of standard Slovenian was built with the CLASSLA-Stanza tool (https://github.com/clarinsi/classla) by training on the SUK training corpus (http://hdl.handle.net/11356/1747) and using the CLARIN.SI-embed.sl word embeddings (http://hdl.handle.net/11356/1204) expanded with the MaCoCu-sl Slovene web corpus (http://hdl.handle.net/11356/1517). The estimated LAS of the parser is ~91.11.
The difference to the previous version of the model is that the model was trained using the SUK training corpus and uses the updated embeddings
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