Many studies in cognitive linguistics have analysed the semantics of 'over', notably the
semantics associated with 'over' as a preposition. Most of them generally conclude that 'over' is
polysemic and this polysemy is to be described thanks to a semantic radial network, showing
the relationships between the different meanings of the word. What we would like to suggest
on the contrary is that the meanings of 'over' are highly dependent on the utterance context in
which its occurrences are embedded, and consequently that the meaning of 'over' itself is
under-specified, rather than polysemic. Moreover, to provide a more accurate account of the
apparent wide range of meanings of 'over' in context, we ought to take into account the other
uses of this unit: as an adverb and particle, and not only as a preposition. In this paper, we
provide a corpus-based description of 'over' which leads us to propose a monosemic definition. ,So as to achiev such a description, we used a short dataset of randomly selected 326 sentences containing 'over' in various positions in the sentences and corresponding to various categories.
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.
Mapping table for the article Hajič et al., 2024: Mapping Czech Verbal Valency to PropBank Argument Labels, in LREC-COLING 2024, as preprocess by the algorithm described in the paper. This dataset i smeant for verification (replicatoin) purposes only. It will b manually processed further to arrive at a workable CzezchpropBank, to be used in Czech UMR annotation, to be further updated during the annotation. The resulting PropBank frame files fir Czech are expected to be available with some future releases of UMR, containing Czech UMR annotation, or separately.
NomVallex 2.0 is a manually annotated valency lexicon of Czech nouns and adjectives, created in the theoretical framework of the Functional Generative Description and based on corpus data (the SYN series of corpora from the Czech National Corpus and the Araneum Bohemicum Maximum corpus). In total, NomVallex is comprised of 1027 lexical units contained in 570 lexemes, covering the following parts-of-speech and derivational categories: deverbal or deadjectival nouns, and deverbal, denominal, deadjectival or primary adjectives. Valency properties of a lexical unit are captured in a valency frame (modeled as a sequence of valency slots, each supplemented with a list of morphemic forms) and documented by corpus examples. In order to make it possible to study the relationship between valency behavior of base words and their derivatives, lexical units of nouns and adjectives in NomVallex are linked to their respective base lexical units (contained either in NomVallex itself or, in case of verbs, in the VALLEX lexicon), linking up to three parts-of-speech (i.e., noun – verb, adjective – verb, noun – adjective, and noun – adjective – verb).
In order to facilitate comparison, this submission also contains abbreviated entries of the base verbs of these nouns and adjectives from the VALLEX lexicon and simplified entries of the covered nouns and adjectives from the PDT-Vallex lexicon.
The NomVallex I. lexicon describes valency of Czech deverbal nouns belonging to three semantic classes, i.e. Communication (dotaz 'question'), Mental Action (plán 'plan') and Psych State (nenávist 'hatred'). It covers both stem-nominals and root-nominals (dotazování se 'asking' and dotaz 'question'). In total, the lexicon includes 505 lexical units in 248 lexemes. Valency properties are captured in the form of valency frames, specifying valency slots and their morphemic forms, and are exemplified by corpus examples.
In order to facilitate comparison, this submission also contains abbreviated entries of the source verbs of these nouns from the Vallex lexicon and simplified entries of the covered nouns from the PDT-Vallex lexicon.
The valency lexicon PDT-Vallex 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). It contains over 11000 valency frames for more than 7000 verbs which occurred in the PDT or PCEDT. It is available in electronically processable format (XML) together with the aforementioned treebanks (to be viewed and edited by TrEd, the PDT/PCEDT main annotation tool), and also in more human readable form including corpus examples (see the WEBSITE link below). 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.
The Prague Dependency Treebank 3.5 is the 2018 edition of the core Prague Dependency Treebank (PDT). It contains all PDT annotation made at the Institute of Formal and Applied Linguistics under various projects between 1996 and 2018 on the original texts, i.e., all annotation from PDT 1.0, PDT 2.0, PDT 2.5, PDT 3.0, PDiT 1.0 and PDiT 2.0, plus corrections, new structure of basic documentation and new list of authors covering all previous editions. The Prague Dependency Treebank 3.5 (PDT 3.5) contains the same texts as the previous versions since 2.0; there are 49,431 annotated sentences (832,823 words) on all layers, from tectogrammatical annotation to syntax to morphology. There are additional annotated sentences for syntax and morphology; the totals for the lower layers of annotation are: 87,913 sentences with 1,502,976 words at the analytical layer (surface dependency syntax) and 115,844 sentences with 1,956,693 words at the morphological layer of annotation (these totals include the annotation with the higher layers annotated as well). Closely linked to the tectogrammatical layer is the annotation of sentence information structure, multiword expressions, coreference, bridging relations and discourse relations.
The Prague Dependency Treebank of Spoken Czech 2.0 (PDTSC 2.0) is a corpus of spoken language, consisting of 742,316 tokens and 73,835 sentences, representing 7,324 minutes (over 120 hours) of spontaneous dialogs. The dialogs have been recorded, transcribed and edited in several interlinked layers: audio recordings, automatic and manual transcripts and manually reconstructed text. These layers were part of the first version of the corpus (PDTSC 1.0). Version 2.0 is extended by an automatic dependency parser at the analytical and by the manual annotation of “deep” syntax at the tectogrammatical layer, which contains semantic roles and relations as well as annotation of coreference.
Supplementary files for a comparative study of word-formation without the addition of derivational affixes (conversion) in English and Czech.
The two .csv files contain 300 verb-noun conversion pairs in English and 300 verb-noun conversion pairs in Czech, i.e. pairs where either the noun is created from the verb or the verb is created from the noun without the use of derivational affixes. In English, the noun and verb in the conversion pair have the same form. In Czech, the noun and verb in the conversion pair differ in inflectional affixes.
The pairs are supplied with manual semantic annotation based on cognitive event schemata.
A file with the Appendix includes a list of dictionary definition phrases used as a basis for the semantic annotation.
The goal of the Uniform Meaning Representation (UMR) project is to design a meaning representation that can be used to annotate the semantic content of a text. UMR is primarily based on Abstract Meaning Representation (AMR), an annotation framework initially designed for English, but also draws from other meaning representations. UMR extends AMR to other languages, particularly morphologically complex, low-resource languages. UMR also adds features to AMR that are critical to semantic interpretation and enhances AMR by proposing a companion document-level representation that captures linguistic phenomena such as coreference as well as temporal and modal dependencies that potentially go beyond sentence boundaries. UMR is intended to be scalable, learnable, and cross-linguistically plausible. It is designed to support both lexical and logical inference.