@inproceedings{uresova-etal-2020-synsemclass,
    title = "{S}yn{S}em{C}lass Linked Lexicon: Mapping Synonymy between Languages",
    author = "Uresova, Zdenka  and
      Fucikova, Eva  and
      Hajicova, Eva  and
      Hajic, Jan",
    editor = "Kernerman, Ilan  and
      Krek, Simon  and
      McCrae, John P.  and
      Gracia, Jorge  and
      Ahmadi, Sina  and
      Kabashi, Besim",
    booktitle = "Proceedings of the 2020 Globalex Workshop on Linked Lexicography",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://aclanthology.org/2020.globalex-1.2",
    pages = "10--19",
    abstract = "This paper reports on an extended version of a synonym verb class lexicon, newly called SynSemClass (formerly CzEngClass). This lexicon stores cross-lingual semantically similar verb senses in synonym classes extracted from a richly annotated parallel corpus, the Prague Czech-English Dependency Treebank. When building the lexicon, we make use of predicate-argument relations (valency) and link them to semantic roles; in addition, each entry is linked to several external lexicons of more or less {``}semantic{''} nature, namely FrameNet, WordNet, VerbNet, OntoNotes and PropBank, and Czech VALLEX. The aim is to provide a linguistic resource that can be used to compare semantic roles and their syntactic properties and features across languages within and across synonym groups (classes, or {'}synsets{'}), as well as gold standard data for automatic NLP experiments with such synonyms, such as synonym discovery, feature mapping, etc. However, perhaps the most important goal is to eventually build an event type ontology that can be referenced and used as a human-readable and human-understandable {``}database{''} for all types of events, processes and states. While the current paper describes primarily the content of the lexicon, we are also presenting a preliminary design of a format compatible with Linked Data, on which we are hoping to get feedback during discussions at the workshop. Once the resource (in whichever form) is applied to corpus annotation, deep analysis will be possible using such combined resources as training data.",
    language = "English",
    ISBN = "979-10-95546-46-7",
}