Universal Derivations (UDer) is a collection of harmonized lexical networks capturing word-formation, especially derivational relations, in a cross-linguistically consistent annotation scheme for many languages. The annotation scheme is based on a rooted tree data structure, in which nodes correspond to lexemes, while edges represent derivational relations or compounding.
The current version of the UDer collection contains eleven harmonized resources covering eleven different languages.
Universal Derivations (UDer) is a collection of harmonized lexical networks capturing word-formation, especially derivational relations, in a cross-linguistically consistent annotation scheme for many languages. The annotation scheme is based on a rooted tree data structure, in which nodes correspond to lexemes, while edges represent derivational relations or compounding. The current version of the UDer collection contains twenty-seven harmonized resources covering twenty different languages.
Universal Derivations (UDer) is a collection of harmonized lexical networks capturing word-formation, especially derivational relations, in a cross-linguistically consistent annotation scheme for many languages. The annotation scheme is based on a rooted tree data structure, in which nodes correspond to lexemes, while edges represent derivational relations or compounding. The current version of the UDer collection contains thirty-one harmonized resources covering twenty-one different languages.
Universal Segmentations (UniSegments) is a collection of lexical resources capturing morphological segmentations harmonised into a cross-linguistically consistent annotation scheme for many languages. The annotation scheme consists of simple tab-separated columns that stores a word and its morphological segmentations, including pieces of information about the word and the segmented units, e.g., part-of-speech categories, type of morphs/morphemes etc. The current public version of the collection contains 38 harmonised segmentation datasets covering 30 different languages.
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.
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
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)
The item contains models to tune for the WMT16 Tuning shared task for Czech-to-English.
CzEng 1.6pre (http://ufal.mff.cuni.cz/czeng/czeng16pre) corpus is used for the training of the translation models. The data is tokenized (using Moses tokenizer), lowercased and sentences longer than 60 words and shorter than 4 words are removed before training. Alignment is done using fast_align (https://github.com/clab/fast_align) and the standard Moses pipeline is used for training.
Two 5-gram language models are trained using KenLM: one only using the CzEng English data and the other is trained using all available English mono data for WMT except Common Crawl.
Also included are two lexicalized bidirectional reordering models, word based and hierarchical, with msd conditioned on both source and target of processed CzEng.