Common Language Resources and Technology Infrastructure - Slovenia
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Ukrainian-English parallel corpus MaCoCu-uk-en 1.0
No full textThe Ukrainian-English parallel corpus MaCoCu-uk-en 1.0 was built by crawling the ".ua" and ".укр" internet top-level domain in 2022, 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
Frequency list of textbook vocabulary by level of education in elementary and secondary schools
No full textThe dataset contains a list of 11906 words (lemmas with part of speech information) and their frequency of occurrence in a corpus of Slovenian textobooks, covering elementary school (Grade 1 to 9) and secondary school (Year 1 to 4). The corpus contains 4,302,857 words (5,373,268 tokens), and consists of 127 textbooks from 16 different subjects. The distribution per school level is as follows:
- Grade 1: 17949 tokens
- Grade 2: 46317 tokens
- Grade 3: 84222 tokens
- Grade 4: 305454 tokens
- Grade 5: 357400 tokens
- Grade 6: 351463 tokens
- Grade 7: 537359 tokens
- Grade 8: 592068 tokens
- Grade 9: 765574 tokens
- Year 1: 665093 tokens
- Year 2: 200267 tokens
- Year 3: 149442 tokens
- Year 4: 23406 tokens
- Year 1-4: 206843 tokens (these are textbooks that are used in all the years of secondary school and were not divided according to different years)
The purpose of the dataset is to facilitate research into vocabularly use at different levels of education, and to enable comparative studies of student language reception and production in Slovene
Word embeddings CLARIN.SI-embed.mk 2.0
No full textCLARIN.SI-embed.mk contains word embeddings induced from a large collection of Macedonian texts crawled from the .mk top-level domain. The embeddings are based on the skip-gram model of fastText trained on 933,231,582 tokens of running text for 986,670 lowercased surface forms.
The difference to the previous version of the embeddings is that this version was trained on the original dataset expanded with the MaCoCu-mk web crawl corpus (http://hdl.handle.net/11356/1512)
Albanian web corpus MaCoCu-sq 1.0
No full textThe Albanian web corpus MaCoCu-sq 1.0 was built by crawling the ".al" internet top-level domain in 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.
A newer version of the corpus is available as part of the MaCoCu-Genre corpora collection at http://hdl.handle.net/11356/1969. The main novelty of the MaCoCu-Genre version is that the texts have been automatically annotated with genre categories. Additionally, the corpus underwent additional post-processing and has been transformed to the JSONL format
Croatian web corpus MaCoCu-hr 2.0
No full textThe Croatian web corpus MaCoCu-hr 2.0 was built by crawling the ".hr" 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. Furthermore, samples from the largest 1,500 domains were manually checked and bad domains, such as machine-translated domains, were removed. 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, as well as larger 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
Open Slovene WordNet OSWN 1.0
No full textOpen Slovene WordNet (OSWN) is derived from Open English WordNet (https://en-word.net/), which itself is derived from Princeton WordNet by the Open English WordNet Community.
OSWN 1.0 contains 95,262 synsets and a total of 164,904 literals. Not all synsets from Open English WordNet were included in this version; approximately 25.000 very terminological synsets (such as those related to specific animal and plant species) require additional analysis and specific expertise. These will be included in later versions.
OSWN 1.0 was constructed by using several relevant existing datasets (sloWNet - http://hdl.handle.net/11356/1026; bilingual dictionaries, thesauri etc.) and methodologies (co-occurrence graphs, machine translation, ChatGPT). Finally, all synsets were manually checked by translators and lexicographers in order to remove inadequate literals. Each synset was checked by one annotator.
The English definitions of synsets were translated automatically using ChatGPT. OSWN 1.0 includes both the English originals as well as their machine translations into Slovene.
For additional information on the content and the format of OSWN, please consult the README file
The CLASSLA-Stanza model for lemmatisation of non-standard Serbian 2.1
No full textThe model for lemmatisation 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) and the ReLDI-NormTagNER-sr corpus (http://hdl.handle.net/11356/1794), using the srLex inflectional lexicon (http://hdl.handle.net/11356/1233). These corpora were additionally augmented for handling missing diacritics by repeating parts of the corpora with diacritics removed. The estimated F1 of the lemma annotations is ~94.92.
The difference to the previous version of the model is that this version is trained on a combination of two corpora (SETimes.SR, ReLDI-NormTagNER-sr)
Icelandic-English parallel corpus MaCoCu-is-en 2.0
No full textThe Icelandic-English parallel corpus MaCoCu-is-en 2.0 was built by crawling the “.is” internet top-level domain in 2021, 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).
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 new version also provides additional metadata, such as the position of the sentence in the paragraph and document, and information whether the sentence is related to a DSI. Moreover, the corpus is now also provided in a document-level format.
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
Serbian-English parallel corpus MaCoCu-sr-en 1.0
No full textThe Serbian-English parallel corpus MaCoCu-sr-en 1.0 was built by crawling the “.rs” and “.срб” internet top-level domains in 2021 and 2022, 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. In each format, the texts are separated based on the script into two files: a Latin and a Cyrillic subcorpus. 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
Bosnian-English parallel corpus MaCoCu-bs-en 1.0
No full textThe Bosnian-English parallel corpus MaCoCu-bs-en 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.
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. In each format, the texts are separated based on the script into two files: a Latin and a Cyrillic subcorpus. 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