CLARIN.SI repository language resources and tools http://hdl.handle.net/11356/1024 2026-11-03T05:18:55Z 2026-11-03T05:18:55Z Baum, Lyman Frank http://hdl.handle.net/11356/2622 2026-11-02T13:37:10Z 2026-11-02T00:00:00Z

Sample from the audiobook "Čarovnik iz Oza" (The wizard of Oz) Baum, Lyman Frank This entry contains the first part of the audiobook "Čarovnik iz Oza" (The Wizard of Oz) by author Lyman Frank Baum, translated to Slovenian by Jernej Županič and narrated by Nika Korenjak (COBISS.ID 293898243, ISBN 978-961-298-519-6). The Wizard of Oz (1900) was first published under the title The Wonderful Wizard of Oz, while the shorter title The Wizard of Oz became established with subsequent reprints. The story begins in the American countryside, where a young girl named Dorothy lives. A violent storm carries her and her little dog Toto away to a fantastical world. As they travel through various lands, they encounter a Scarecrow, a Lion, a Tin Woodman, Oz, the ruler of the Emerald City, good and wicked witches, and other inhabitants. Their journey is full of exciting adventures and obstacles, which are ultimately overcome thanks to the ingenuity of the characters, helpful companions, and a magical object (the silver shoes), leading to a happy ending, as good always triumphs over evil. In addition to The Wizard of Oz, Baum published sixteen further books featuring characters and fictional settings from the Land of Oz. Numerous adaptations and reinterpretations of Baum's works belonging to the Oz series are also available on the global market. As early as the beginning of the twentieth century, a musical adaptation was staged, and the book has since inspired various film adaptations and theatrical productions, while its characters have become part of popular culture.

2026-11-02T00:00:00Z Erjavec, Tomaž http://hdl.handle.net/11356/2610 2026-11-02T16:51:00Z 2026-10-29T00:00:00Z

Test item for API file upload Erjavec, Tomaž This item is meant for testing API upload of bitstreams.

2026-10-29T00:00:00Z Štravs, Miha Dobrovoljc, Kaja Bezgovšek, Luka http://hdl.handle.net/11356/2607 2026-10-24T09:06:53Z 2026-05-13T00:00:00Z

Service for querying dependency treebanks Drevesnik 1.3 Štravs, Miha; Dobrovoljc, Kaja; Bezgovšek, Luka Drevesnik (https://orodja.cjvt.si/drevesnik/) is an online service for querying Slovenian corpora parsed with the Universal Dependencies annotation scheme. It features an easy-to-use query language on the one hand and user-friendly graph visualizations on the other. It is based on the open-source dep_search tool (https://github.com/TurkuNLP/dep_search), which was localized and modified so as to also support querying by JOS morphosyntactic tags, random distribution of results, and filtering by sentence length. The source code and the documentation for the search backend and the web user interface are publicly available on the CLARIN.SI GitHub repository https://github.com/clarinsi/drevesnik. In comparison to previous version (1.2), release 1.3 introduces some improved interface features, such as playing audio recordings, documenting the query specifications, and linking them to unique URLs.

2026-05-13T00:00:00Z Ljubešić, Nikola Dinić, Vuk Rupnik, Peter http://hdl.handle.net/11356/1972 2026-10-15T12:45:31Z 2026-10-13T00:00:00Z

Dataset on genre and news topic persistence between versions 1 and 2 of CLASSLA-web Ljubešić, Nikola; Dinić, Vuk; Rupnik, Peter The dataset contains metadata on documents from the CLASSLA-web v2 web crawl (http://hdl.handle.net/11356/2079), describing each document by its (1) document identifier, (2) URL, (3) web domain, (4) predicted genre, (5) predicted topic, (6) the length in terms of number of characters and, most importantly, (7) whether the document is considered to have a near-duplicate in the CLASSLA-web v1 corpus collection. This dataset originated from the paper “What Persists across Web Crawls? Genre and News Topic Variation in South Slavic Web Corpora”, accepted for publication at the 13th Web-as-Corpus Workshop (WaC-13), co-located with EMNLP 2026. We make the dataset openly available to facilitate follow-up research on this topic.

2026-10-13T00:00:00Z Kosem, Iztok Čibej, Jaka Dobrovoljc, Kaja Erjavec, Tomaž Ljubešić, Nikola Ponikvar, Primož Šinkec, Mihael Krek, Simon http://hdl.handle.net/11356/2572 2026-10-05T17:20:57Z 2026-10-05T00:00:00Z

Monitor corpus of Slovene Trendi 2026-09 Kosem, Iztok; Čibej, Jaka; Dobrovoljc, Kaja; Erjavec, Tomaž; Ljubešić, Nikola; Ponikvar, Primož; Šinkec, Mihael; Krek, Simon The Trendi corpus is a monitor corpus of Slovenian. It contains news articles from 106 media websites, published by 64 publishers. Trendi 2026-09 covers the period from January 2019 to September 2026, complementing the Gigafida 2.2 reference corpus of written Slovene (http://hdl.handle.net/11356/2106). 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. If you would like to use the dataset for research purposes, please contact Iztok Kosem (iztok.kosem@ijs.si). This version adds texts from September 2026.

2026-10-05T00:00:00Z Pollak, Senja Jemec Tomazin, Mateja Arhar Holdt, Špela Thi Hong Tran, Hanh http://hdl.handle.net/11356/2258 2026-09-15T09:56:14Z 2026-09-15T00:00:00Z

Slovenian Social Assistance Rights Terminology SSAR-TERM 1.0 Pollak, Senja; Jemec Tomazin, Mateja; Arhar Holdt, Špela; Thi Hong Tran, Hanh SSAR-TERM 1.0 is a manually validated Slovenian terminology resource for the domain of social assistance rights. The terms were automatically extracted from the SSAR 1.0 collection of legally binding and explanatory texts (http://hdl.handle.net/11356/1985) using a transformer-based term extraction model. The extracted term candidates were subsequently reviewed by a terminology specialist. Of the 1,000 extracted candidates, 983 were retained as valid terms and assigned to one of three domain categories: 1 – terms from the domain of social assistance rights (533) 2 – terms from closely related domains (358) 3 – terms from other domains (92) The columns provide automatically assigned part-of-speech (POS) labels, the extracted term form, the canonical (dictionary) form, which was manually verified and corrected where necessary, and the manually assigned domain label (1, 2, or 3; see above). The resource is intended primarily for research on terminology and terminography. If you use this resource, please cite: Senja POLLAK, Mateja JEMEC TOMAZIN, Eva PORI, Hanh Thi Hong TRAN, Špela ARHAR HOLDT (2026). Luščenje terminologije pravic socialnega varstva. In Proceedings of Language Technologies and Digital Humanities: Proceedings of the Conference. Ljubljana, September 17–18, 2026.

2026-09-15T00:00:00Z Žgank, Andrej Verdonik, Darinka Boto, Sanja Donaj, Gregor http://hdl.handle.net/11356/2484 2026-09-14T16:29:21Z 2026-08-31T00:00:00Z

Gail Valley dialect E2E Automatic Speech Recognition model Zila 1.0 Žgank, Andrej; Verdonik, Darinka; Boto, Sanja; Donaj, Gregor This resource provides automatic speech recognition (ASR) models for the Slovenian Gail Valley dialect, developed using the open-source WeNet 3.1 framework. The models were trained on 84.25 hours of speech from the Corpus of the Gail Valley dialect Zila 1.0 (http://hdl.handle.net/11356/2257). The system employs a hybrid CTC/attention architecture with a Conformer encoder and Transformer decoder. The log-Mel filterbank features were used for training. The final averaged model achieves a word error rate of 24.64% and a character error rate of 12.05%. Models are provided in WeNet and ONNX formats, supporting local, server-based, and GPU-accelerated inference and deployment.

2026-08-31T00:00:00Z Martinc, Matej Dimitrievska, Mihaela http://hdl.handle.net/11356/2317 2026-09-13T10:01:22Z 2026-08-07T00:00:00Z

Multimodal document understanding dataset from "Ilustrirani Slovenec" Martinc, Matej; Dimitrievska, Mihaela This entry contains the Ilustrirani Slovenec Multimodal Document Understanding Dataset, a benchmark dataset designed for training and evaluating vision-language models on historical Slovenian newspaper pages. The dataset is derived from Ilustrirani Slovenec, a historical illustrated weekly supplement of the newspaper Slovenec, published between 1924 and 1932 and available through the Digital Library of Slovenia (dLib.si): https://www.dlib.si/details/URN:NBN:SI:spr-XWQWZFUW. A total of 410 newspaper issues were collected in PDF format and split into individual page images, resulting in 2,566 newspaper pages. Visual regions on the pages were automatically detected using Segment Anything Model 3 (SAM3; https://ai.meta.com/research/sam3/), using prompts targeting common visual elements found in historical newspapers, including photographs, portraits, illustrations, sketches, cartoons, maps, diagrams, advertisements, and posters. The resulting detections were post-processed to remove unsuitable, overlapping, and nested regions. In total, the detection pipeline identified 14,805 image regions. The detected image regions and the corresponding newspaper pages were subsequently processed using Gemini 3 Pro Preview (https://ai.google.dev/gemini-api/docs/models/gemini-3-pro-preview). For each page, the model generated structured annotations containing image-region bounding boxes, corresponding image captions where available, and page-level Slovenian text excluding image captions. The original Slovenian text was preserved without translation, normalization, or spelling correction. The generated annotations were transformed into six task-specific multimodal instruction-following datasets, each distributed with predefined training and test files: dataset1_train.jsonl and dataset1_test.jsonl contain the image region to caption task, where a bounding box corresponding to an image region is provided and the model must predict the associated image caption. dataset2_train.jsonl and dataset2_test.jsonl contain the caption to image region task, where an image caption is provided and the model must identify the corresponding image region by predicting its bounding-box coordinates. dataset3_train.jsonl and dataset3_test.jsonl contain the image region detection task, where the model must identify all image regions present on a newspaper page. dataset4_train.jsonl and dataset4_test.jsonl contain the image region detection and captioning task, where image regions must be detected and the corresponding caption generated for each detected region. dataset5_train.jsonl and dataset5_test.jsonl contain the page-level text extraction task, where all textual content on a newspaper page, including headlines, article text, and other textual elements, must be extracted while excluding image captions. dataset6_train.jsonl and dataset6_test.jsonl contain the full-page understanding task, requiring extraction of the complete structured content of a newspaper page, including page text, image-region locations, and corresponding image captions. Datasets 1 and 2 are constructed from individual image-caption pairs, while Datasets 3–6 are constructed at the page level. Of the 2,566 extracted newspaper pages, 2,556 were included in the final benchmark; 10 pages did not yield valid final annotations during the automatic processing pipeline. The benchmark is distributed with predefined training and test splits, comprising 2,300 training pages and 256 test pages. The same page-level partition is applied consistently across all six datasets so that no newspaper page appears in both the training and test partitions. Manual Quality Assessment To verify the quality of the automatically generated annotations, a subset of the test data was manually evaluated. Ten examples were randomly sampled from each of the six benchmark tasks, resulting in 60 evaluated benchmark instances. Each example was independently assessed by two annotators using two task-specific binary quality criteria, resulting in a total of 120 binary quality assessments. The two annotators agreed on 118 of the 120 assessments, corresponding to an observed agreement of 98.33%. Inter-annotator agreement was high, with Cohen's kappa of 0.8483 and Gwet's AC1 of 0.9813. Agreement was 100% for all evaluated criteria except joint localization and caption generation in Dataset 4 and structural hierarchy in Dataset 6, which both achieved 90% agreement. All manually reviewed examples that passed both task-specific quality criteria according to both annotators were additionally aggregated into manually_verified_minibench.jsonl. This file contains 53 manually verified examples across the six benchmark tasks and can be used as an additional high-confidence evaluation benchmark. Accessing the Corresponding Newspaper Pages The original Ilustrirani Slovenec PDF files and page images are not redistributed as part of this resource. The corresponding newspaper issues are available through the Digital Library of Slovenia (dLib.si): https://www.dlib.si/details/URN:NBN:SI:spr-XWQWZFUW. The Python script download_and_split_ilustrirani_slovenec.py is included for downloading the corresponding PDF issues and converting them into individual page images. The generated page images correspond to the image identifiers referenced in the six datasets and in the manually verified mini-benchmark.

2026-08-07T00:00:00Z Žgank, Andrej Donaj, Gregor Sereinig, Uši Koren-Zwitter, Tatjana Boto, Sanja Zwitter-Grilc, Sabina Kanzian, Martina Hren, Karl Vidinić, Jasna Verdonik, Darinka http://hdl.handle.net/11356/2257 2026-09-08T09:43:09Z 2026-08-31T00:00:00Z

Corpus of the Gail Valley dialect Zila 1.0 Žgank, Andrej; Donaj, Gregor; Sereinig, Uši; Koren-Zwitter, Tatjana; Boto, Sanja; Zwitter-Grilc, Sabina; Kanzian, Martina; Hren, Karl; Vidinić, Jasna; Verdonik, Darinka The Zila Speech Corpus is a spoken language resource documenting the Slovenian Gail Valley dialect, an endangered Slovenian minority dialect spoken in Austria. The corpus contains 206 recordings in WAV format, with a total duration of approximately 100 hours and 7 minutes, produced by 74 anonymized speakers. It was created within the LINGUA project to support the development of automatic speech recognition for the Gail Valley dialect, as well as linguistic research, education, corpus search, and long-term dialect documentation and preservation. The corpus includes different types of speech material: private speech, public informal speech, public formal speech, and read speech. The largest part of the corpus consists of private recordings, while the remaining material documents public, institutional, and read speech contexts. The recordings cover topics such as autobiographical narratives, Gail Valley identity, folk tales, local customs and rituals, rural work and farming, family and village networks, local history, memories, and community life. The recordings in WAV format are accompanied by manually prepared, time-aligned transcriptions in Transcriber .trs format. The transcription follows an extended orthographic approach: dialectal morphological and syntactic features, hesitations, fillers, and self-corrections are preserved, while the transcription is not intended as a full phonetic transcription. Personal data in the transcripts are anonymized using the marker [anonim], and the corresponding sections in the audio are masked with a beep before publication. Beside the .trs format, the transcriptions in EXMARaLDA format (.exb, .exs) were also generated. Speaker metadata include anonymized speaker ID, gender, age, first language, bilingualism, dialect information, dialect retention, education, residence information, spontaneity of speech, and notes. Recording metadata include recording and transcription identifiers, language, title, date, source, recording location, genre, speech type, channel, number of speakers, recording device, recording setting, original format, audio quality, length, and notes.

2026-08-31T00:00:00Z Vrabl, Klara Vintar, Špela Brglez, Mojca http://hdl.handle.net/11356/2358 2026-09-08T08:18:30Z 2026-08-25T00:00:00Z

Slovenian Word Associations with Explanations WAX-sl Vrabl, Klara; Vintar, Špela; Brglez, Mojca WAX-sl is the first Slovenian word association dataset that pairs cue–association pairs with a short explanatory sentence and, for a subset of the data, a semantic relation label. It adapts the methodology of the English WAX (Word Association eXplanations) dataset (Liu et al., 2022) to Slovenian. The 100 cue words were randomly sampled from the Reference List of Slovenian Frequent Common Words (Arhar Holdt et al., 2020), balanced by part of speech (34 nouns, 33 verbs, 33 adjectives). Data were elicited in two phases from 9 paid annotators (students of linguistics/digital linguistics), each cue answered by 4 participants: in Phase 1 participants gave up to three free associations per cue, and in Phase 2 they wrote a short sentence (3–15 words) explaining the relation between cue and association. This produced 1,261 cue–association–explanation triples. A subset of 700 triples (covering all 100 cues) was further annotated with a semantic relation label using an extended version of the WAX relation taxonomy, comprising 21 fine-grained labels across five coarse categories (Concept-Properties, Situational, Taxonomic, Linguistic, None-of-the-Above). Each item was independently labelled by two of five trained annotators and adjudicated by a third curator; inter-annotator agreement (pairwise Cohen's κ) was 0.46. The remaining 561 triples were not selected for relation annotation and are marked NA in the Relation column. The dataset is released as a CSV file with the columns: ID (unique triple identifier), CUE (cue word), ASSOCIATION (association given in response to the cue), EXPLANATION (explanatory sentence linking cue and association), and RELATION (semantic relation label, or NA if not annotated). WAX-sl was created as part of the master's thesis "A multilingual comparison of human-generated and LLM-generated associations" (Vrabl, 2026), which also compares this human dataset against associations generated by three LLMs (Gemma 3, GaMS, Magistral Small). References: Holdt, Špela Arhar, Senja Pollak, Marko Robnik Šikonja, and Simon Krek. 2020. ‘Referenčni Seznam Pogostih Splošnih Besed Za Slovenščino’. Conference on Language Technologies & Digital Humanities. http://hdl.handle.net/11356/1346. Liu, Chunhua, Trevor Cohn, Simon De Deyne, and Lea Frermann. 2022. WAX: A New Dataset for Word Association eXplanations. In Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 106–120, Online only. Association for Computational Linguistics. https://aclanthology.org/2022.aacl-main.9/ Vrabl, Klara. 2026. A multilingual comparison of human-generated and LLM-generated associations. Master's thesis, Faculty of Arts, University of Ljubljana, http://hdl.handle.net/20.500.12556/RUL-184558.

2026-08-25T00:00:00Z