The segment of Československý zvukový týdeník Aktualita (Czechoslovak Aktualita Sound Newsreel), 1938, issue no. 26 promotes the State Defence Jubilee Fund intended for modernizing the Czechoslovak Army, and asks for contributions to be made to account no. 400 at the Postal Savings Bank.
The database actually contains two sets of recordings, both recorded in the moving or stationary vehicles (passenger cars or trucks). All data were recorded within the project “Intelligent Electronic Record of the Operation and Vehicle Performance” whose aim is to develop a voice-operated software for registering the vehicle operation data.
The first part (full_noises.zip) consists of relatively long recordings from the vehicle cabin, containing spontaneous speech from the vehicle crew. The recordings are accompanied with detailed transcripts in the Transcriber XML-based format (.trs). Due to the recording settings, the audio contains many different noises, only sparsely interspersed with speech. As such, the set is suitable for robust estimation of the voice activity detector parameters.
The second set (prompts.zip) consists of short prompts that were recorded in the controlled setting – the speakers either answered simple questions or they repeated commands and short phrases. The prompts were recorded by 26 different speakers. Each speaker recorded at least two sessions (with identical set of prompts) – first in stationary vehicle, with low level of noise (those recordings are marked by –A_ in the file name) and second while actually driving the car (marked by –B_ or, since several speakers recorded 3 sessions, by –C_). The recordings from this set are suitable mostly for training of the robust domain-specific speech recognizer and also ASR test purposes.
Segment from Český zvukový týdeník Aktualita (Czech Aktualita Sound Newsreel) issue no. 46B from 1943 presents footage of the voluntary work and help with harvesting organised by the Board of Trustees for the Education of Youth as part of mandatory service. Older teenagers worked at railway stations, unloading potatoes.
STYX 1.0 is a corpus of Czech sentences selected from the Prague Dependency treebank. The criterion for including sentences into STYX was their suitability for practicing Czech morphology and syntax in elementary schools. The sentences contain both the PDT annotations and the school sentence analyses. The school sentence analyses were created by transforming the PDT annotations using handcrafted rules. Altogether the STYX 1.0 corpus contains 11 655 sentences.
Originally, the STYX 1.0 corpus was an inseparable part of the Styx system (http://hdl.handle.net/11858/00-097C-0000-0001-48FB-F)
STYX 1.0 is a corpus of Czech sentences selected from the Prague Dependency treebank. The criterion for including sentences into STYX was their suitability for practicing Czech morphology and syntax in elementary schools. The sentences contain both the PDT annotations and the school sentence analyses. The school sentence analyses were created by transforming the PDT annotations using handcrafted rules. Altogether the STYX 1.0 corpus contains 11 655 sentences.
Originally, the STYX 1.0 corpus was an inseparable part of the Styx system (http://hdl.handle.net/11858/00-097C-0000-0001-48FB-F)
This entry contains the SumeCzech dataset and the metric RougeRAW used for evaluation. Both the dataset and the metric are described in the paper "SumeCzech: Large Czech News-Based Summarization Dataset" by Milan Straka et al.
The dataset is distributed as a set of Python scripts which download the raw HTML pages from CommonCrawl and then process them into the required format.
The MPL 2.0 license applies to the scripts downloading the dataset and to the RougeRAW implementation.
Note: sumeczech-1.0-update-230225.zip is the updated release of the SumeCzech download script, including the original RougeRAW evaluation metric. The download script was modified to use the updated CommonCraw download URL and to support Python 3.10 and Python 3.11. However, the downloaded dataset is still exactly the same. The original archive sumeczech-1.0.zip was renamed to sumeczech-1.0-obsolete-180213.zip and is kept for reference.
SumeCzech-NER
SumeCzech-NER contains named entity annotations of SumeCzech 1.0 (Straka et al. 2018, SumeCzech: Large Czech News-Based Summarization Dataset).
Format
The dataset is split into four files. Files are in jsonl format. There is one JSON object on each line of the file. The most important fields of JSON objects are:
- dataset: train, dev, test, oodtest
- ne_abstract: list of named entity annotations of article's abstract
- ne_headline: list of named entity annotations of article's headline
- ne_text: list of name entity annotations of article's text
- url: article's URL that can be used to match article across SumeCzech and SumeCzech-NER
Annotations
We used SpaCy's NER model trained on CoNLL-based extended CNEC 2.0. The model achieved a 78.45 F-Score on the dataset's testing set. The annotations are in IOB2 format. The entity types are: Numbers in addresses, Geographical names, Institutions, Media names, Artifact names, Personal names, and Time expressions.
Tokenization
We used the following Python code for tokenization:
from typing import List
from nltk.tokenize import word_tokenize
def tokenize(text: str) -> List[str]:
for mark in ('.', ',', '?', '!', '-', '–', '/'):
text = text.replace(mark, f' {mark} ')
tokens = word_tokenize(text)
return tokens
The SynSemClass synonym verb lexicon is a result of a project investigating semantic ‘equivalence’ of verb senses and their valency behavior in parallel Czech-English language resources, i.e., relating verb meanings with respect to contextually-based verb synonymy. The lexicon entries are linked to PDT-Vallex (http://hdl.handle.net/11858/00-097C-0000-0023-4338-F), EngVallex (http://hdl.handle.net/11858/00-097C-0000-0023-4337-2), CzEngVallex (http://hdl.handle.net/11234/1-1512), FrameNet (https://framenet.icsi.berkeley.edu/fndrupal/), VerbNet (http://verbs.colorado.edu/verbnet/index.html), PropBank (http://verbs.colorado.edu/%7Empalmer/projects/ace.html), Ontonotes (http://verbs.colorado.edu/html_groupings/), and English Wordnet (https://wordnet.princeton.edu/). Part of the dataset are files reflecting interannotator agreement.
The SynSemClass 3.5 synonym verb lexicon investigates semantic ‘equivalence’ of verb senses and their valency behavior in parallel Czech-English and German-English language resources, i.e., relates verb meanings with respect to contextually-based verb synonymy.
The Czech lexicon entries are linked to PDT-Vallex (http://hdl.handle.net/11858/00-097C-0000-0023-4338-F), Vallex (http://hdl.handle.net/11234/1-3524), and CzEngVallex (http://hdl.handle.net/11234/1-1512).
The English lexicon entries are linked to EngVallex (http://hdl.handle.net/11858/00-097C-0000-0023-4337-2), CzEngVallex (http://hdl.handle.net/11234/1-1512), FrameNet (https://framenet.icsi.berkeley.edu/fndrupal/), VerbNet (https://uvi.colorado.edu/ and http://verbs.colorado.edu/verbnet/index.html), PropBank (http://propbank.github.io/), Ontonotes (http://clear.colorado.edu/compsem/index.php?page=lexicalresources&sub=ontonotes), and English Wordnet (https://wordnet.princeton.edu/).
The German lexicon entries are linked to Woxikon (https://synonyme.woxikon.de), E-VALBU (https://grammis.ids-mannheim.de/verbvalenz), and GUP (http://alanakbik.github.io/multilingual.html; https://github.com/UniversalDependencies/UD_German-GSD).