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.
LiFR-Law is a corpus of Czech legal and administrative texts with measured reading comprehension and a subjective expert annotation of diverse textual properties based on the Hamburg Comprehensibility Concept (Langer, Schulz von Thun, Tausch, 1974). It has been built as a pilot data set to explore the Linguistic Factors of Readability (hence the LiFR acronym) in Czech administrative and legal texts, modeling their correlation with actually observed reading comprehension. The corpus is comprised of 18 documents in total; that is, six different texts from the legal/administration domain, each in three versions: the original and two paraphrases. Each such document triple shares one reading-comprehension test administered to at least thirty readers of random gender, educational background, and age. The data set also captures basic demographic information about each reader, their familiarity with the topic, and their subjective assessment of the stylistic properties of the given document, roughly corresponding to the key text properties identified by the Hamburg Comprehensibility Concept.
LiFR-Law is a corpus of Czech legal and administrative texts with measured reading comprehension and a subjective expert annotation of diverse textual properties based on the Hamburg Comprehensibility Concept (Langer, Schulz von Thun, Tausch, 1974). It has been built as a pilot data set to explore the Linguistic Factors of Readability (hence the LiFR acronym) in Czech administrative and legal texts, modeling their correlation with actually observed reading comprehension. The corpus is comprised of 18 documents in total; that is, six different texts from the legal/administration domain, each in three versions: the original and two paraphrases. Each such document triple shares one reading-comprehension test administered to at least thirty readers of random gender, educational background, and age. The data set also captures basic demographic information about each reader, their familiarity with the topic, and their subjective assessment of the stylistic properties of the given document, roughly corresponding to the key text properties identified by the Hamburg Comprehensibility Concept.
Changes to the previous version and helpful comments
• File names of the comprehension test results (self-explanatory)
• Corrected one erroneous automatic evaluation rule in the multiple-choice evaluation (zahradnici_3,
TRUE and FALSE had been swapped)
• Evaluation protocols for both question types added into Folder lifr_formr_study_design
• Data has been cleaned: empty responses to multiple-choice questions were re-inserted. Now, all surveys
are considered complete that have reader’s subjective text evaluation complete (these were placed at
the very end of each survey).
• Only complete surveys (all 7 content questions answered) are represented. We dropped the replies of
six users who did not complete their surveys.
• A few missing responses to open questions have been detected and re-inserted.
• The demographic data contain all respondents who filled in the informed consent and the demographic
details, with respondents who did not complete any test survey (but provided their demographic
details) in a separate file. All other data have been cleaned to contain only responses by the regular
respondents (at least one completed survey).