EngVallex 2.0 as a slightly updated version of EngVallex. It is the English counterpart of the PDT-Vallex valency lexicon, using the same view of valency, valency frames and the description of a surface form of verbal arguments. EngVallex contains links also to PropBank (English predicate-argument lexicon). The EngVallex lexicon is fully linked to the English side of the PCEDT parallel treebank(s), which is in fact the PTB re-annotated using the Prague Dependency Treebank style of annotation. The EngVallex is available in an XML format in our repository, and also in a searchable form with examples from the PCEDT. EngVallex 2.0 is the same dataset as the EngVallex lexicon packaged with the PCEDT 3.0 corpus, but published separately under a more permissive licence, avoiding the need for LDC licence which is tied to PCEDT 3.0 as a whole.
A richly annotated and genre-diversified language resource, The Prague Dependency Treebank – Consolidated 1.0 (PDT-C 1.0, or PDT-C in short in the sequel) is a consolidated release of the existing PDT-corpora of Czech data, uniformly annotated using the standard PDT scheme. PDT-corpora included in PDT-C: Prague Dependency Treebank (the original PDT contents, written newspaper and journal texts from three genres); Czech part of Prague Czech-English Dependency Treebank (translated financial texts, from English), Prague Dependency Treebank of Spoken Czech (spoken data, including audio and transcripts and multiple speech reconstruction annotation); PDT-Faust (user-generated texts). The difference from the separately published original treebanks can be briefly described as follows: it is published in one package, to allow easier data handling for all the datasets; the data is enhanced with a manual linguistic annotation at the morphological layer and new version of morphological dictionary is enclosed; a common valency lexicon for all four original parts is enclosed. Documentation provides two browsing and editing desktop tools (TrEd and MEd) and the corpus is also available online for searching using PML-TQ.