VPS-30-En is a small lexical resource that contains the following 30 English verbs: access, ally, arrive, breathe,
claim, cool, crush, cry, deny, enlarge, enlist, forge, furnish, hail, halt, part, plough, plug, pour, say, smash, smell, steer, submit, swell,
tell, throw, trouble, wake and yield. We have created and have been using VPS-30-En to explore the interannotator agreement potential
of the Corpus Pattern Analysis. VPS-30-En is a small snapshot of the Pattern Dictionary of English Verbs (Hanks and Pustejovsky,
2005), which we revised (both the entries and the annotated concordances) and enhanced with additional annotations. and This work has been partly supported by the Ministry of
Education of CR within the LINDAT-Clarin project
LM2010013, and by the Czech Science Foundation under
the projects P103/12/G084, P406/2010/0875 and
P401/10/0792.
VPS-GradeUp is a collection of triple manual annotations of 29 English verbs based on the Pattern Dictionary of English Verbs (PDEV) and comprising the following lemmas: abolish, act, adjust, advance, answer, approve, bid, cancel, conceive, cultivate, cure, distinguish, embrace, execute, hire, last, manage, murder, need, pack, plan, point, praise, prescribe, sail, seal, see, talk, urge . It contains results from two different tasks:
1. Graded decisions
2. Best-fit pattern (WSD) .
In both tasks, the annotators were matching verb senses defined by the PDEV patterns with 50 actual uses of each verb (using concordances from the BNC [2]). The verbs were randomly selected from a list of completed PDEV lemmas with at least 3 patterns and at least 100 BNC concordances not previously annotated by PDEV’s own annotators. Also, the selection excluded verbs contained in VPS-30-En[3], a data set we developed earlier. This data set was built within the project Reviving Zellig S. Harris: more linguistic information for distributional lexical analysis of English and Czech and in connection with the SemEval-2015 CPA-related task.
Vystadial 2013 is a dataset of telephone conversations in English and Czech, developed for training acoustic models for automatic speech recognition in spoken dialogue systems. It ships in three parts: Czech data, English data, and scripts.
The data comprise over 41 hours of speech in English and over 15 hours in Czech, plus orthographic transcriptions. The scripts implement data pre-processing and building acoustic models using the HTK and Kaldi toolkits.
This is the Czech data part of the dataset. and This research was funded by the Ministry of
Education, Youth and Sports of the Czech Republic under the grant agreement
LK11221.
Vystadial 2013 is a dataset of telephone conversations in English and Czech, developed for training acoustic models for automatic speech recognition in spoken dialogue systems. It ships in three parts: Czech data, English data, and scripts.
The data comprise over 41 hours of speech in English and over 15 hours in Czech, plus orthographic transcriptions. The scripts implement data pre-processing and building acoustic models using the HTK and Kaldi toolkits.
This is the English data part of the dataset. and This research was funded by the Ministry of
Education, Youth and Sports of the Czech Republic under the grant agreement
LK11221.
Vystadial 2013 is a dataset of telephone conversations in English and Czech, developed for training acoustic models for automatic speech recognition in spoken dialogue systems. It ships in three parts: Czech data, English data, and scripts.
The data comprise over 41 hours of speech in English and over 15 hours in Czech, plus orthographic transcriptions. The scripts implement data pre-processing and building acoustic models using the HTK and Kaldi toolkits.
This is the scripts part of the dataset. and This research was funded by the Ministry of
Education, Youth and Sports of the Czech Republic under the grant agreement
LK11221.
This is the Czech data collected during the `VYSTADIAL` project. It is an extension of the 'Vystadial 2013' Czech part data release. The dataset comprises of telephone conversations in Czech, developed for training acoustic models for automatic speech recognition in spoken dialogue systems.
A set of corpora for 120 languages automatically collected from wikipedia and the web.
Collected using the W2C toolset: http://hdl.handle.net/11858/00-097C-0000-0022-60D6-1
A tool used to build multilingual corpora from wikipedia. Download the web pages, convert them to plain text, identify language, etc.
A set of 120 corpora collected using this tool is available at https://ufal-point.mff.cuni.cz/xmlui/handle/11858/00-097C-0000-0022-6133-9
We provide the Vietnamese version of the multi-lingual test set from WMT 2013 [1] competition. The Vietnamese version was manually translated from English. For completeness, this record contains the 3000 sentences in all the WMT 2013 original languages (Czech, English, French, German, Russian and Spanish), extended with our Vietnamese version. Test set is used in [2] to evaluate translation between Czech, English and Vietnamese.
References
1. http://www.statmt.org/wmt13/evaluation-task.html
2. Duc Tam Hoang and Ondřej Bojar, The Prague Bulletin of Mathematical Linguistics. Volume 104, Issue 1, Pages 75--86, ISSN 1804-0462. 9/2015
Testing set from WMT 2011 [1] competition, manually translated from Czech and English into Slovak. Test set contains 3003 sentences in Czech, Slovak and English. Test set is described in [2].
References:
[1] http://www.statmt.org/wmt11/evaluation-task.html
[2] Petra Galuščáková and Ondřej Bojar. Improving SMT by Using Parallel Data of a Closely Related Language. In Human Language Technologies - The Baltic Perspective - Proceedings of the Fifth International Conference Baltic HLT 2012, volume 247 of Frontiers in AI and Applications, pages 58-65, Amsterdam, Netherlands, October 2012. IOS Press. and The work on this project was supported by the grant EuroMatrixPlus (FP7-ICT-
2007-3-231720 of the EU and 7E09003 of the Czech Republic)