This package contains data sets for development and testing of machine translation of sentences from summaries of medical articles between Czech, English, French, and German. and This work was supported by the EU FP7 project Khresmoi (European Comission contract No. 257528). The language resources are distributed by the LINDAT/Clarin project of the Ministry of Education, Youth and Sports of the Czech Republic (project no. LM2010013). We thank all the data providers and copyright holders for providing the source data and anonymous experts for translating the sentences.
This package contains data sets for development (Section dev) and testing (Section test) of machine translation of sentences from summaries of medical articles between Czech, English, French, German, Hungarian, Polish, Spanish
and Swedish. Version 2.0 extends the previous version by adding Hungarian, Polish, Spanish, and Swedish translations.
Statistical spell- and (occasional) grammar-checker. There are three versions: a unix command line utility and an OS X SpellServer with a System Service, that integrates with native OS X GUI applications, and a web service run by Lindat-Clarin, that can be used either through a web form in a browser, or by web applications using API. and The LINDAT-CLARIN project (LM2010013), fully supported by TheMinistry of Education, Sports and Youth of The Czech Republic under the programme LM of "Large Infrastructures"
Korektor is a statistical spell-checker and (occasionally) grammar-checker. It is released under 2-Clause BSD license http://opensource.org/licenses/BSD-2-Clause.
Korektor started with Michal Richter's diploma thesis Advanced Czech Spellchecker https://redmine.ms.mff.cuni.cz/documents/1, but it is being developed further. There are two versions: a command line utility (tested on Linux, Windows and OS X) and a REST service with publicly available API http://lindat.mff.cuni.cz/services/korektor/api-reference.php and HTML front end https://lindat.mff.cuni.cz/services/korektor/.
KUK 0.0 is a pilot version of a corpus of Czech legal and administrative texts designated as data for manual and automatic assessment of accessibility (comprehensibility or clarity) of Czech legal texts.
We present a large corpus of Czech parliament plenary sessions. The corpus
consists of approximately 444 hours of speech data and corresponding text
transcriptions. The whole corpus has been segmented to short audio snippets
making it suitable for both training and evaluation of automatic speech
recognition (ASR) systems. The source language of the corpus is Czech, which
makes it a valuable resource for future research as only a few public datasets
are available for the Czech language.
"Large Scale Colloquial Persian Dataset" (LSCP) is hierarchically organized in asemantic taxonomy that focuses on multi-task informal Persian language understanding as a comprehensive problem. LSCP includes 120M sentences from 27M casual Persian tweets with its dependency relations in syntactic annotation, Part-of-speech tags, sentiment polarity and automatic translation of original Persian sentences in five different languages (EN, CS, DE, IT, HI).
The LatinISE corpus is a text corpus collected from the LacusCurtius, Intratext and Musisque Deoque websites. Corpus texts have rich metadata containing information as genre, title, century or specific date.
This Latin corpus was built by Barbara McGillivray.
In the version 4 of the corpus the high frequency lemmas have been manually corrected and sentence boundaries have been added.
Lexical Annotation Workbench (LAW) is an integrated environment for morphological annotation. It supports simple morphological annotation (assigning a lemma and tag to a word), integration and comparison of different annotations of the same text, searching for particular word, tag etc.
This dataset contains annotation of PDT using Czech WordNet ontology: http://hdl.handle.net/11858/00-097C-0000-0001-4880-3
Data is stored in PML format. This is a stand-off annotation and for most use cases it requires PDT 2.0 and the Czech WordNet 1.9 PDT that we have used for annotation. and 1ET100300517, 1ET201120505