Common Language Resources and Technology Infrastructure - Slovenia
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The CLASSLA-Stanza model for lemmatisation of standard Macedonian 2.1
No full textThe model for lemmatisation of standard Macedonian was built with the CLASSLA-Stanza tool (https://github.com/clarinsi/classla-stanfordnlp) by training on the 1984 training corpus expanded with the Macedonian SETimes corpus (to be published). The estimated F1 of the lemma annotations is ~98.81.
The difference from the previous version is that this version was trained using a larger training dataset
Corpus of scientific texts of contemporary Slovenian KZB 1.0
No full textThe Corpus of scientific texts of contemporary Slovenian consists of 25 million words from scientific monographs and scientific papers written mainly between 2000 and 2023. It was designed as one of the resources of the project eSSKJ and corpus - towards state-of-the-art language data.
The corpus is linguistically annotated with the CLASSLA pipeline (https://github.com/clarinsi/classla/) at the levels of tokenization, sentence segmentation, lemmatization, MULTEXT-East v6 MSD-tags (https://nl.ijs.si/ME/V6/msd/html/msd-sl.html), JOS dependency syntax (https://nl.ijs.si/jos/bib/jos-skladnja-navodila.pdf), and named entities (https://nl.ijs.si/janes/wp-content/uploads/2017/09/SlovenianNER-eng-v1.1.pdf).
The corpus is available in the CoNLL-U format, as well as vertical files for use with Sketch Engine type concordancers
The CLASSLA-Stanza model for morphosyntactic annotation of non-standard Croatian 2.1
No full textThis model for morphosyntactic annotation of non-standard Croatian was built with the CLASSLA-Stanza tool (https://github.com/clarinsi/classla) by training on the hr500k training corpus (http://hdl.handle.net/11356/1792) and the ReLDI-NormTagNER-hr corpus (http://hdl.handle.net/11356/1793), using the CLARIN.SI-embed.hr word embeddings (http://hdl.handle.net/11356/1790). 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 ~92.49.
The difference to the previous version of the model is that this version uses the new version of Croatian word embeddings and is trained on a combination of two datasets (hr500k, ReLDI-NormTagNER-hr)
The CLASSLA-Stanza model for lemmatisation of standard Croatian 2.1
No full textThe model for lemmatisation of standard Croatian was built with the CLASSLA-Stanza tool (https://github.com/clarinsi/classla) by training on the hr500k training corpus (http://hdl.handle.net/11356/1792) and using the hrLex inflectional lexicon (http://hdl.handle.net/11356/1232). The estimated F1 of the lemma annotations is ~98.02.
The difference to the previous version is that this version was trained on the new version of the hr500k corpus
Greek-English parallel corpus MaCoCu-el-en 1.0
No full textThe Greek-English parallel corpus MaCoCu-el-en 1.0 was built by crawling the “.gr", ".ελ", ".cy" and ".eu" internet top-level domain in 2023, extending the crawl dynamically to other domains as well.
All the crawling process was carried out by the MaCoCu crawler (https://github.com/macocu/MaCoCu-crawler). Websites containing documents in both target languages were identified and processed using the tool Bitextor (https://github.com/bitextor/bitextor). Considerable effort was devoted into cleaning the extracted text to provide a high-quality parallel corpus. This was achieved by removing boilerplate and near-duplicated paragraphs and documents that are not in one of the targeted languages. Document and segment alignment as implemented in Bitextor were carried out, and Bifixer (https://github.com/bitextor/bifixer) and BicleanerAI (https://github.com/bitextor/bicleaner-ai) were used for fixing, cleaning, and deduplicating the final version of the corpus.
The corpus is available in three formats: two sentence-level formats, TXT and TMX, and a document-level TXT format. TMX is an XML-based format and TXT is a tab-separated format. They both consist of pairs of source and target segments (one or several sentences) and additional metadata. The following metadata is included in both sentence-level formats:
- source and target document URL;
- paragraph ID which includes information on the position of the sentence in the paragraph and in the document (e.g., “p35:77s1/3” which means “paragraph 35 out of 77, sentence 1 out of 3”);
- quality score as provided by the tool Bicleaner AI (a likelihood of a pair of sentences being mutual translations, provided with a score between 0 and 1);
- similarity score as provided by the sentence alignment tool Bleualign (value between 0 and 1);
- personal information identification (“biroamer-entities-detected”): segments containing personal information are flagged, so final users of the corpus can decide whether to use these segments;
- translation direction and machine translation identification (“translation-direction”): the source segment in each segment pair was identified by using a probabilistic model (https://github.com/RikVN/TranslationDirection), which also determines if the translation has been produced by a machine-translation system;
- a DSI class (“dsi”): information whether the segment is connected to any of Digital Service Infrastructure (DSI) classes (e.g., cybersecurity, e-health, e-justice, open-data-portal), defined by the Connecting Europe Facility (https://github.com/RikVN/DSI);
- English language variant: the language variant of English (British or American, using a lexicon-based English variety classifier - https://pypi.org/project/abclf/) was identified on document and domain level.
Furthermore, the sentence-level TXT format provides additional metadata:
- web domain of the text;
- source and target document title;
- the date when the original file was retrieved;
- the original type of the file (e.g., “html”), from which the sentence was extracted;
- paragraph quality (labels, such as “short” or “good”, assigned based on paragraph length, URL and stopword density via the jusText tool - https://corpus.tools/wiki/Justext);
- information whether the sentence is a heading or not in the original document.
The document-level TXT format provides pairs of documents identified to contain parallel data. In addition to the parallel documents (in base64 format), the corpus includes the following metadata: source and target document URL, a DSI category and the English language variant (British or American).
Notice and take down: Should you consider that our data contains material that is owned by you and should therefore not be reproduced here, please: (1) Clearly identify yourself, with detailed contact data such as an address, telephone number or email address at which you can be contacted. (2) Clearly identify the copyrighted work claimed to be infringed. (3) Clearly identify the material that is claimed to be infringing and information reasonably sufficient in order to allow us to locate the material. (4) Please write to the contact person for this resource whose email is available in the full item record. We will comply with legitimate requests by removing the affected sources from the next release of the corpus.
This action has received funding from the European Union's Connecting Europe Facility 2014-2020 - CEF Telecom, under Grant Agreement No. INEA/CEF/ICT/A2020/2278341. This communication reflects only the author’s view. The Agency is not responsible for any use that may be made of the information it contains
Dependency tree extraction tool STARK 2.0
No full textSTARK is a python-based command-line tool for extraction of dependency trees from parsed corpora, aimed at corpus-driven linguistic investigations of syntactic and lexical phenomena of various kinds. It takes a treebank in the CONLL-U format as input and returns a list of all relevant dependency trees with frequency information and other useful statistics, such as the strength of association between the nodes of a tree, or its significance in comparison to another treebank.
For installation, execution and the description of various user-defined parameter settings, see the official project page at: https://github.com/clarinsi/STARK
In comparison with v1, this version introduces several new features and improvements, such as the option to set parameters in the command line, compare treebanks or visualise results online
Croatian Twitter training corpus ReLDI-NormTagNER-hr 3.0
No full textReLDI-NormTagNER-hr 3.0 is a manually annotated corpus of Croatian tweets. It is meant as a gold-standard training and testing dataset for tokenisation, sentence segmentation, word normalisation, morphosyntactic tagging, lemmatisation and named entity recognition of non-standard Croatian. Each tweet is also annotated for its automatically assigned standardness levels (T = technical standardness, L = linguistic standardness).
This version of the dataset has various annotation errors corrected and the dataset encoded in the CoNLL-U-Plus format, similar to other manually annotated linguistic datasets for Croatian and Serbian.
The continuous improvement of this dataset is led by the CLASSLA knowledge centre for South Slavic languages (https://www.clarin.si/info/k-centre/) and the ReLDI Centre Belgrade
Bulgarian web corpus MaCoCu-bg 2.0
No full textThe Bulgarian web corpus MaCoCu-bg 2.0 was built by crawling the ".bg" and ".бг" internet top-level domains in 2021, extending the crawl dynamically to other domains as well. The crawler is available at https://github.com/macocu/MaCoCu-crawler.
Considerable effort was devoted into cleaning the extracted text to provide a high-quality web corpus. This was achieved by removing boilerplate (https://corpus.tools/wiki/Justext) and near-duplicated paragraphs (https://corpus.tools/wiki/Onion), discarding very short texts as well as texts that are not in the target language. The dataset is characterized by extensive metadata which allows filtering the dataset based on text quality and other criteria (https://github.com/bitextor/monotextor), making the corpus highly useful for corpus linguistics studies, as well as for training language models and other language technologies.
In XML format, each document is accompanied by the following metadata: title, crawl date, url, domain, file type of the original document, distribution of languages inside the document, and a fluency score based on a language model. The text of each document is divided into paragraphs that are accompanied by metadata on the information whether a paragraph is a heading or not, metadata on the paragraph quality (labels, such as “short” or “good”, assigned based on paragraph length, URL and stopword density via the jusText tool - https://corpus.tools/wiki/Justext) and fluency (score between 0 and 1, assigned with the Monocleaner tool - https://github.com/bitextor/monocleaner), the automatically identified language of the text in the paragraph, and information whether the paragraph contains sensitive information (identified via the Biroamer tool - https://github.com/bitextor/biroamer).
As opposed to the previous version, this version has more accurate metadata on languages of the texts, which was achieved by using Google's Compact Language Detector 2 (CLD2) (https://github.com/CLD2Owners/cld2), a high-performance language detector supporting many languages. Other tools, used for web corpora creation and curation, have been updated as well, resulting in an even cleaner corpus.
The corpus can be easily read with the prevert parser (https://pypi.org/project/prevert/).
Notice and take down: Should you consider that our data contains material that is owned by you and should therefore not be reproduced here, please: (1) Clearly identify yourself, with detailed contact data such as an address, telephone number or email address at which you can be contacted. (2) Clearly identify the copyrighted work claimed to be infringed. (3) Clearly identify the material that is claimed to be infringing and information reasonably sufficient in order to allow us to locate the material. (4) Please write to the contact person for this resource whose email is available in the full item record. We will comply with legitimate requests by removing the affected sources from the next release of the corpus.
This action has received funding from the European Union's Connecting Europe Facility 2014-2020 - CEF Telecom, under Grant Agreement No. INEA/CEF/ICT/A2020/2278341. This communication reflects only the author’s view. The Agency is not responsible for any use that may be made of the information it contains
Maltese web corpus MaCoCu-mt 2.0
No full textThe Maltese web corpus MaCoCu-mt 2.0 was built by crawling the ".mt" internet top-level domain in 2021, extending the crawl dynamically to other domains as well. The crawler is available at https://github.com/macocu/MaCoCu-crawler.
Considerable effort was devoted into cleaning the extracted text to provide a high-quality web corpus. This was achieved by removing boilerplate (https://corpus.tools/wiki/Justext) and near-duplicated paragraphs (https://corpus.tools/wiki/Onion), discarding very short texts as well as texts that are not in the target language. The dataset is characterized by extensive metadata which allows filtering the dataset based on text quality and other criteria (https://github.com/bitextor/monotextor), making the corpus highly useful for corpus linguistics studies, as well as for training language models and other language technologies.
In XML format, each document is accompanied by the following metadata: title, crawl date, url, domain, file type of the original document, distribution of languages inside the document, and a fluency score based on a language model. The text of each document is divided into paragraphs that are accompanied by metadata on the information whether a paragraph is a heading or not, metadata on the paragraph quality (labels, such as “short” or “good”, assigned based on paragraph length, URL and stopword density via the jusText tool - https://corpus.tools/wiki/Justext) and fluency (score between 0 and 1, assigned with the Monocleaner tool - https://github.com/bitextor/monocleaner), the automatically identified language of the text in the paragraph, and information whether the paragraph contains sensitive information (identified via the Biroamer tool - https://github.com/bitextor/biroamer).
As opposed to the previous version, this version has more accurate metadata on languages of the texts, which was achieved by using Google's Compact Language Detector 2 (CLD2) (https://github.com/CLD2Owners/cld2), a high-performance language detector supporting many languages. Other tools, used for web corpora creation and curation, have been updated as well, resulting in an even cleaner corpus.
The corpus can be easily read with the prevert parser (https://pypi.org/project/prevert/).
Notice and take down: Should you consider that our data contains material that is owned by you and should therefore not be reproduced here, please: (1) Clearly identify yourself, with detailed contact data such as an address, telephone number or email address at which you can be contacted. (2) Clearly identify the copyrighted work claimed to be infringed. (3) Clearly identify the material that is claimed to be infringing and information reasonably sufficient in order to allow us to locate the material. (4) Please write to the contact person for this resource whose email is available in the full item record. We will comply with legitimate requests by removing the affected sources from the next release of the corpus.
This action has received funding from the European Union's Connecting Europe Facility 2014-2020 - CEF Telecom, under Grant Agreement No. INEA/CEF/ICT/A2020/2278341. This communication reflects only the author’s view. The Agency is not responsible for any use that may be made of the information it contains
Bosnian web corpus MaCoCu-bs 1.0
No full textThe Bosnian web corpus MaCoCu-bs 1.0 was built by crawling the ".ba" internet top-level domain in 2021 and 2022, extending the crawl dynamically to other domains as well. The crawler is available at https://github.com/macocu/MaCoCu-crawler.
Considerable effort was devoted into cleaning the extracted text to provide a high-quality web corpus. This was achieved by removing boilerplate (https://corpus.tools/wiki/Justext) and near-duplicated paragraphs (https://corpus.tools/wiki/Onion), discarding very short texts as well as texts that are not in the target language. The dataset is characterized by extensive metadata which allows filtering the dataset based on text quality and other criteria (https://github.com/bitextor/monotextor), making the corpus highly useful for corpus linguistics studies, as well as for training language models and other language technologies.
In XML format, each document is accompanied by the following metadata: title, crawl date, url, domain, file type of the original document, distribution of languages inside the document, and a fluency score based on a language model. The text of each document is divided into paragraphs that are accompanied by metadata on the information whether a paragraph is a heading or not, metadata on the paragraph quality (labels, such as “short” or “good”, assigned based on paragraph length, URL and stopword density via the jusText tool - https://corpus.tools/wiki/Justext) and fluency (score between 0 and 1, assigned with the Monocleaner tool - https://github.com/bitextor/monocleaner), the automatically identified language of the text in the paragraph, and information whether the paragraph contains sensitive information (identified via the Biroamer tool - https://github.com/bitextor/biroamer).
The corpus can be easily read with the prevert parser (https://pypi.org/project/prevert/).
Notice and take down: Should you consider that our data contains material that is owned by you and should therefore not be reproduced here, please: (1) Clearly identify yourself, with detailed contact data such as an address, telephone number or email address at which you can be contacted. (2) Clearly identify the copyrighted work claimed to be infringed. (3) Clearly identify the material that is claimed to be infringing and information reasonably sufficient in order to allow us to locate the material. (4) Please write to the contact person for this resource whose email is available in the full item record. We will comply with legitimate requests by removing the affected sources from the next release of the corpus.
This action has received funding from the European Union's Connecting Europe Facility 2014-2020 - CEF Telecom, under Grant Agreement No. INEA/CEF/ICT/A2020/2278341. This communication reflects only the author’s view. The Agency is not responsible for any use that may be made of the information it contains