Annotated corpus of 350 decision of Czech top-tier courts (Supreme Court, Supreme Administrative Court, Constitutional Court).
Every decision is annotated by two trained annotators and then manually adjudicated by one trained curator to solve possible disagreements between annotators. Adjudication was conducted non-destructively, therefore dataset contains all original annotations.
Corpus was developed as training and testing material for reference recognition tasks. Dataset contains references to other court decisions and literature. All references consist of basic units (identifier of court decision, identification of court issuing referred decision, author of book or article, title of book or article, point of interest in referred document etc.), values (polarity, depth of discussion etc.).
Annotated corpus of 350 decision of Czech top-tier courts (Supreme Court, Supreme Administrative Court, Constitutional Court).
Every decision is annotated by two trained annotators and then manually adjudicated by one trained curator to solve possible disagreements between annotators. Adjudication was conducted non-destructively, therefore corpus (raw) contains all original annotations.
Corpus was developed as training and testing material for reference recognition tasks. Dataset contains references to other court decisions and literature. All references consist of basic units (identifier of court decision, identification of court issuing referred decision, author of book or article, title of book or article, point of interest in referred document etc.), values (polarity, depth of discussion etc.).
Lexicon of Czech verbal multiword expressions (VMWEs) used in Parseme Shared Task 2017. https://typo.uni-konstanz.de/parseme/index.php/2-general/142-parseme-shared-task-on-automatic-detection-of-verbal-mwes
Lexicon consists of 4785 VMWEs, categorized into four categories according to Parseme Shared Task (PST) typology: IReflV (inherently reflexive verbs), LVC (light verb constructions), ID (idiomatic expressions) and OTH (other VMWEs with other than verbal syntactic head).
Verbal multiword expressions as well as deverbative variants of VMWEs were annotated during the preparation phase of PST. These data were published as http://hdl.handle.net/11372/LRT-2282. Czech part includes 14,536 VMWE occurences:
1611 ID
10000 IReflV
2923 LVC
2 OTH
This lexicon was created out of Czech data. Each lexicon entry is represented by one line in the form:
type lemmas frequency PoS [used form 1; used form 2; ... ]
(columns are separated by tabs) where:
type ... is the type of VMWE in PST typology
lemmas ... are space separated lemmatized forms of all words that constitutes the VMWE
frequency ... is the absolute frequency of this item in PST data
PoS ... is a space separated list of parts of speech of individual words (in the same order as in "lemmas")
final field contains a list of all (1 to 18) used forms found in the data (since Czech is a flective language).
This corpus consists of full transcriptions of both Democratic and Republican 2016 presidential candidate debates, with a special focus on the idiolects of Hillary Clinton and Donald Trump against the background of the speeches of other candidates for the post of president of the United States.
The transcriptions are sourced from the American Presidency Project at the University of California, Santa Barbara. Any use of the material requires a prior and explicit written permission by the project administrator (contact policy@ucsb.edu). This corpus material is now being shared with their kindly permission.
This resource is the second version of an Italian morphological dictionary for content words, encoded in a JSON Lines format text file. It contains correspondences between surface form and lexical forms of words followed by standard grammatical properties. Compared to the first release, this version has a better JSON structure. The surface word forms have been generated algorithmically by using stable phonological and morphological rules of the Italian language. Particular attention has been given to the generation of verbs for which rules have been extracted from A.L e G. Lepschy, La Lingua Italiana. The dictionary with its remarkable coverage is particularly useful used together with the Italian Function Words v2 (http://hdl.handle.net/11372/LRT-2629) for tasks such as pos-tagging or syntactic parsing.
This resource is the third version of the Italian morphological dictionary for content words (http://hdl.handle.net/11372/LRT-2630), encoded in a JSON Lines format. Compared to the previous version, it contains some minor improvements.
This dictionary is the second version of 11372/LRT-2288, a curated list of Italian function words in a JSON Lines format text file, particularly useful for tasks such as POS-Tagging or Syntactic Parsing. It contains 999 single-word forms and 2501 multi-words forms. Each entry may have the following grammatical features: lemma, pos, mood, tense, person, number, gender, case, degree. Compared to the first release, this version has a more clear JSON structure.
This dictionary is the third version of 11372/LRT-2288, a curated list of Italian function words in a JSON Lines format text file, particularly useful for tasks such as part of speech tagging or syntactic parsing. Compared to the previous release, this version includes some minor improvements.
The NottDeuYTSch corpus contains over 33 million words taken from approximately 3 million YouTube comments from videos published between 2008 to 2018 targeted at a young, German-speaking demographic and represents an authentic language snapshot of young German speakers. The corpus was proportionally sampled based on video category and year from a database of 112 popular German-speaking YouTube channels in the DACH region for optimal representativeness and balance and contains a considerable amount of associated metadata for each comment that enable further longitudinal cross-sectional analyses.
The NottDeuYTSch corpus contains over 33 million words taken from approximately 3 million YouTube comments from videos published between 2008 to 2018 targeted at a young, German-speaking demographic and represents an authentic language snapshot of young German speakers. The corpus was proportionally sampled based on video category and year from a database of 112 popular German-speaking YouTube channels in the DACH region for optimal representativeness and balance and contains a considerable amount of associated metadata for each comment that enable further longitudinal cross-sectional analyses.