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.
The valency lexicon PDT-Vallex 4.0 has been built in close connection with the annotation of the Prague Dependency Treebank project (PDT) and its successors (mainly the Prague Czech-English Dependency Treebank project, PCEDT, the spoken language corpus (PDTSC) and corpus of user-generated texts in the project Faust). It contains over 14500 valency frames for almost 8500 verbs which occurred in the PDT, PCEDT, PDTSC and Faust corpora. In addition, there are nouns, adjectives and adverbs, linked from the PDT part only, increasing the total to over 17000 valency frames for 13000 words. All the corpora have been published in 2020 as the PDT-C 1.0 corpus with the PDT-Vallex 4.0 dictionary included; this is a copy of the dictionary published as a separate item for those not interested in the corpora themselves. It is available in electronically processable format (XML), and also in more human readable form including corpus examples (see the WEBSITE link below, and the links to its main publications elsewhere in this metadata). The main feature of the lexicon is its linking to the annotated corpora - each occurrence of each verb is linked to the appropriate valency frame with additional (generalized) information about its usage and surface morphosyntactic form alternatives. It replaces the previously published unversioned edition of PDT-Vallex from 2014.
VALLEX 4.0 provides information on the valency structure (combinatorial potential) of verbs in their particular senses; each sense is by a gloss and examples. VALLEX 4.0 describes almost 4 700 Czech verbs in more than 11 000 lexical units, i.e., given verbs in the given senses. VALLEX 4.0 is a is a collection of linguistically annotated data and documentation, resulting from an attempt at formal description of valency frames of Czech verbs. In order to satisfy different needs of different potential users, the lexicon is distributed (i) in a HTML version (the data allows for an easy and fast navigation through the lexicon) and (ii) in a machine-tractable form, so that the VALLEX data can be used in NLP applications. VALLEX 4.0 provides (in addition to information from previous versions) also characteristics of verbs expressing reciprocity and reflexivity.
The data is provided in two formats: XML and JSON.
VALLEX 4.5 provides information on the valency structure (combinatorial potential) of Czech verbs in their particular senses (almost 4 700 verbs in more than 11 080 lexical units, supplemented with more than 290 nouns in more than 350 lexical units forming complex predicates with light verbs). VALLEX 4.5 is an enhanced successor of VALLEX 3.0, 3.5, and 4.0. In addition to the information stored there, VALLEX 4.5 provides a detailed description of reflexive verbs, i.e., verbs with the reflexive "se" or "si" as an obligatory part of their verb lexemes. VALLEX 4.5 covers 1 525 reflexive verbs in 1 545 lexical units (2 501 when aspectual counterparts counted separately). In order to satisfy different needs of different potential users, the lexicon is distributed (i) online in a HTML version (the data allows for an easy and fast navigation through the lexicon) and (ii) in this distribution in a machine-tractable form, so that the VALLEX data can be used in NLP applications.