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.
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.
Czech translation of WordSim353. The Czech translation of English WordSim353 word pairs were obtained from four translators. All translation variants were scored according to the lexical similarity/relatedness annotation instructions for WordSim353 annotators, by 25 Czech annotators. The resulting data set consists of two annotation files: "WordSim353-cs.csv" and "WordSim-cs-Multi.csv". Both files are encoded in UTF-8, have a header, text is enclosed in double quotes, and columns are separated by commas. The rows are numbered. The WordSim-cs-Multi data set has rows numbered from 1 to 634, whereas the row indices in the WordSim353-cs data set reflect the corresponding row numbers in the WordSim-cs-Multi data set.
The WordSim353-cs file contains a one-to-one mapping selection of 353 Czech equivalent pairs whose judgments have proven to be most similar to the judgments of their corresponding English originals (compared by the absolute value of the difference between the means over all annotators in each language counterpart). In one case ("psychology-cognition"), two Czech equivalent pairs had identical means as well as confidence intervals, so we randomly selected one.
The "WordSim-cs-Multi.csv" file contains human judgments for all translation variants.
In both data sets, we preserved all 25 individual scores. In the WordSim353-cs data set, we added a column with their Czech means as well as a column containing the original English means and 95% confidence intervals in separate columns for each mean (computed by the CI function in the Rmisc R package). The WordSim-cs-Multi data set contains only the Czech means and confidence intervals. For the most convenient lexical search, we provided separate columns with the respective Czech and English single words, entire word pairs, and eventually an English-Czech quadruple in both data sets.
The data set also contains an xls table with the four translations and a preliminary selection of the best variants performed by an adjudicator.