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35 - datasetVytauto Didžiojo universitetasThe Pedagogic Corpus of Lithuanian is a monolingual specialized corpus, prepared for learning and teaching Lithuanian in a foreign language classroom. The pedagogic corpus includes authentic Lithuanian texts, selected using such criteria as a learner relevant communicative function and genre. Spoken language as well as written language are represented in the corpus. The size of the corpus is 669,000 tokens: 111,000 tokens from texts and spoken language for A1–A2 levels, 558,000 tokens from texts and spoken language for B1–B2 levels (according to the Common European Framework of Reference for Languages). The spoken component constitutes appr. 7.5 % of the Corpus. The written subpart of the corpus (containing 620,000 tokens) includes levelled texts from coursebooks and unlevelled texts from other sources. The texts from coursebooks and other sources could be classified into 29 text types (dialogs, narratives, information, etc.) and 4 groups according to the communicative aims: informational texts, educational texts, advertising and fiction. There are two types of searches in the corpus: simple and advanced (see „Search Tips“). Simple Search allows you to find instances of a search item (word form, lemma, two words) in the whole corpus, or particular part of the corpus (spoken or written texts). After selecting the written subcorpus, you can further select the text type (coursebooks or non coursebook texts) and/or the genre of the written texts. Advanced Search allows you to use all the features of simple search and find some additional options. Since the Pedagogic Corpus is morphologically annotated, the advanced search allows you to search by grammatical features (e.g. part of speech, case, number, verb form, etc.).
44 428 142 - datasetVytauto Didžiojo universitetasThe Database of Lithuanian multiword expressions (MWEs) is freely accessible for online search at: https://resursai.pastovu.vdu.lt/paieska/paprastoji from 2019. It contains two-word and three-word MWEs extracted from the DELFI.lt corpus representing news texts on the various topics (https://klc.vdu.lt/pastovuSearch.html). First, 12,000 MWEs (mostly collocations, a few idioms) were included in the database. In 2022, the database was updated adding new collocations from the same corpus and filtering arbitrary collocations: out of appr. 19,000 collocations appr. 9000 are marked as arbitrary collocations, i.e., having lexical collocability restrictions. The database provides rich information about the usage of collocations: lemma, word forms, frequencies (in the DELFI.lt corpus), morphological information, syntactic relations, grammatical variants, text genres, and usage examples. Usage variation cases are also illustrated, for example, word order changes or insertions between collocation constituents.
75 - datasetVytauto Didžiojo universitetasWordlist of the Contemporary Corpus of Lithuanian language. Corpus Structure. Subcorpus Words,m Proportion: Fiction 17,08 12,3%; Non-fiction 22,09; 15,9% Documents; 13,54 9,7% Periodicals; 85,80 61,7%; Speech Corpus 0,54 0,4% --- Total 139,05 100%. Website: tekstynas.vdu.lt corpus.vdu.lt Date: 17/10/2016 Method: > cat *txt | tr ' ' '\ > ' | tr '[:upper:]' '[:lower:]' | tr -d '[:punct:]' | grep -v '[^a-z]' | grep -v "^\s*$" | sort | uniq -c | sort -rn > freq-visas.txt Reference Rimkutė E., Kovalevskaitė J., Melninkaitė V., Utka A., Vitkutė-Adžgauskienė D. 2010: Corpus of Contemporary Lithuanian Language – the Standardised Way. Proceedings of the Fourth International Conference Human Language Technologies – The Baltic Perspective, 154–160. Licence: CLARIN-LT PUB
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367 1410 428 142 69 472 34 176 - datasetVDU mokslo valdymo sistema VDU CRIS, 2021This dataset is the result of a survey conducted during the 2012-2016 COST Action FP1202 (MaP-FGR): Strengthening conservation: a key issue for adaptation of marginal/peripheral populations of forest trees to climate change in Europe. It contains the name, location and other relevant information of 577 forests that are considered as marginal of peripheral by forest experts. Information (as specified in the read-me file) includes among others: Genus, Species, Country, Name of marginal population, Latitude, Longitude, Elevation, Type of marginality, Silvicultural system, Protection status, Threat status, and genetic and phenotypic trait data availability. This dataset can be used to, e.g., test effects of marginality on biological and genetic traits, compare expert knowledge with statistics-based indicators, raise awareness of the importance of marginal populations for conservation and sustainable use.
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Boizou, Loic 13
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