Common Language Resources and Technology Infrastructure - Slovenia
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Academic Dictionary of the Modern Russian Language (ELEXIS)
No full textСловарь современного русского литературного языка. This is the largest completed Academic dictionary of the Russian language. It spans 17 volumes and covers the vocabulary from the end of the 18th century until the 1960s.
The dictionary was available on line at iling.spb.ru/departements/modern_lexicography/bas/about.html.ru, however, the link seems to have stopped working
The CLASSLA-StanfordNLP model for named entity recognition of standard Croatian 1.0
No full textThis model for named entity recognition of 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/11356/1183) and using the CLARIN.SI-embed.hr word embeddings (http://hdl.handle.net/11356/1205)
The CLASSLA-StanfordNLP model for JOS dependency parsing of standard Slovenian 1.0
No full textThe model for JOS dependency parsing of standard Slovenian was built with the CLASSLA-StanfordNLP tool (https://github.com/clarinsi/classla-stanfordnlp) by training on the JOS-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 ~93.5
A Resource for Evaluating Graded Word Similarity in Context: CoSimLex
No full textThe dataset contains human similarity ratings for pairs of words. The annotators were presented with contexts that contained both of the words in the pair and the dataset features two different contexts per pair. The words were sourced from the English, Croatian, Finnish and Slovenian versions of the original Simlex dataset
The CLASSLA-StanfordNLP model for named entity recognition of non-standard Slovenian 1.0
No full textThis model for named entity recognition of non-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 the Janes-Tag training corpus (http://hdl.handle.net/11356/1238), using the CLARIN.SI-embed.sl word embeddings (http://hdl.handle.net/11356/1204). The training corpora were additionally augmented for handling missing diacritics by repeating parts of the corpora with diacritics removed
Sentiment Annotated Dataset of Croatian News
No full textWe present a collection of sentiment annotations for news articles (article links) in Croatian language. A set of 2025 news articles was gathered from 24sata, one of the leading media companies in Croatia with the highest circulation. 6 annotators annotated the articles on the document level using a five-level Likert scale (1—very negative, 2—negative, 3—neutral, 4—positive, and 5—very positive). The final sentiment of an instance was defined as the average of the sentiment scores given by the different annotators. An instance was labeled as negative, if the average of given scores was less than or equal to 2.4; neutral, if the average of given scores was between 2.4 and 3.6; or positive, if the average of given scores was greater than or equal to 3.6. The annotation guidelines correspond to the Slovenian sentiment-annotated collection of news SentiNews 1.0 (http://hdl.handle.net/11356/1110).
If you use the dataset, please cite the following paper (which contains also the details on the dataset creation, and on monolingual and cross-lingual sentiment classification experiments): Pelicon, A.; Pranjić, M.; Miljković, D.; Škrlj, B.; Pollak, S. Zero-Shot Learning for Cross-Lingual News Sentiment Classification. Appl. Sci. 2020, 10, 5993. https://doi.org/10.3390/app1017599
Multilingual comparable corpora of parliamentary debates ParlaMint 1.0
No full textParlaMint is a multilingual set of comparable corpora containing parliamentary debates mostly starting at the end of 2015 and extending to mid-2020, with each corpus being about 20 million words in size. The sessions in the corpora are marked as belonging to the COVID-19 period (after October 2019), or being "reference" (before that date).
The corpora have extensive meta-data about the speakers (name, gender, party affiliation, MP status), are structured into time-stamped terms, sessions and meetings, with each speech being marked by its speaker and their role (chair, regular speaker). The speeches also contain marked-up transcriber comments, such as gaps in the transcription, interruptions, applause, etc.
The corpora are encoded according to the Parla-CLARIN TEI recommendation, but have been validated to the compatible but much stricter ParlaMint schemas. The schemas are included in the distribution, along with scripts to convert the corpora into other formats. The ZIP files with the TEI encoded corpora also include the automatically derived plain text version of the corpus, along with metadata on the speeches.
In addition to the ParlaMint TEI encoded corpora, their linguistically encoded variants (".ana") are also available. The annotation includes named entities, lemmatisation, part-of-speech tagging, and morphological features and syntactic parses according to the Universal Dependencies recommendations. State-of-the-art tools have been used to perform the annotations. The .ana.zip corpora include the ParlaMint encoded XML, as well as derived formats, in particular, CoNLL-U and vertical files
Consonant-vowel structures in the GOS 1.0 corpus 1.1
No full textThe lists contain consonant-vowel structures of all lemmas, word forms, and standardized word forms in the GOS 1.0 Corpus of Spoken Slovene (http://hdl.handle.net/11356/1040). In each unit, its characters were converted as follows: C - consonant (in lists with finegrained character categorizations, consonants were divided into Z - sonorant, G - voiced obstruent, and K - voiceless obstruent), V - vowel, X - foreign consonant, Y - foreign vowel, S - symbol, P - punctuation, N - number, F - non-Latin-script character, ! - other.
Each consonant-vowel structure also contains its frequency in the corpus (i.e. the total sum of the frequencies of all units corresponding to the consonant-vowel structure), as well as the set of all units (in the lists labeled "entire") or the set of its 30 most frequent units (in the lists labeled as "short"), along with their part-of-speech categories and their individual frequencies). They also contain the number of all unique units within the consonant-vowel structure.
The lists were prepared based on frequency lists extracted from GOS 1.0 using LIST: http://hdl.handle.net/11356/1276
Note that there exists a related resource, "Consonant-vowel structures in the Gigafida 2.0 corpus", http://hdl.handle.net/11356/1289
Compared to the previous version (http://hdl.handle.net/11356/1290), this one includes fixes of several typos and substitutes all instances of "normalized forms" with the more adequate term "standardized forms" (as used in the SSJ project)
Annotated corpus of Croatian language-related news articles MetaLangNEWS-Hr
No full textA comprehensive corpus of news articles on the topic of language, published in major Croatian daily newspapers and news portals in the five-year period of January 1, 2015 - January 1, 2020. The corpus is designed to facilitate research on metalanguage (‘language about language’), linguistic ideologies, language policy and planning, as well as the specific contemporary debates on language defining, naming, and standardisation, ongoing in post-Yugoslav societies.
The corpus has been tagged using the CLASSLA-StanfordNLP models for morphosyntactic annotation and lemmatisation of standard Croatian. The corpus is available in plain text version, XML with full metadata, and tagged CONLL-U format.
MetaLangNEWS-Hr is complemented with a separate corpus of citizen metalanguage comments, i.e. online comments to the news articles, available as MetaLangNEWS-COMMENTS-Hr (http://hdl.handle.net/11356/1370). Parallel versions from Slovenia (http://hdl.handle.net/11356/1360) and Serbia (http://hdl.handle.net/11356/1371) are also available
The Orange workflow for observing collocation clusters ColEmbed 1.0
No full textThe Orange Workflow for Observing Collocation Clusters ColEmbed 1.0
ColEmbed is a workflow (.OWS file) for Orange Data Mining (an open-source machine learning and data visualization software: https://orangedatamining.com/) that allows the user to observe clusters of collocation candidates extracted from corpora. The workflow consists of a series of data filters, embedding processors, and visualizers.
As input, the workflow takes a tab-separated file (.TSV/.TAB) with data on collocations extracted from a corpus, along with their relative frequencies by year of publication and other optional values (such as information on temporal trends). The workflow allows the user to select the features which are then used in the workflow to cluster collocation candidates, along with the embeddings generated based on the selected lemmas (either one lemma or both lemmas can be selected, depending on our clustering criteria; for instance, if we wish to cluster adjective+noun candidates based on the similarities of their noun components, we only select the second lemma to be taken into account in embedding generation). The obtained embedding clusters can be visualized and further processed (e.g. by finding the closest neighbors of a reference collocation). The workflow is described in more detail in the accompanying README file.
The entry also contains three .TAB files that can be used to test the workflow. The files contain collocation candidates (along with their relative frequencies per year of publication and four measures describing their temporal trends; see http://hdl.handle.net/11356/1424 for more details) extracted from the Gigafida 2.0 Corpus of Written Slovene (https://viri.cjvt.si/gigafida/) with three different syntactic structures (as defined in http://hdl.handle.net/11356/1415):
1) p0-s0 (adjective + noun, e.g. rezervni sklad),
2) s0-s2 (noun + noun in the genitive case, e.g. ukinitev lastnine), and
3) gg-s4 (verb + noun in the accusative case, e.g. pripraviti besedilo).
It should be noted that only collocation candidates with absolute frequency of 15 and above were extracted.
Please note that the ColEmbed workflow requires the installation of the Text Mining add-on for Orange. For installation instructions as well as a more detailed description of the different phases of the workflow and the measures used to observe the collocation trends, please consult the README file