@inproceedings{pradet-etal-2014-adapting,
    title = "Adapting {V}erb{N}et to {F}rench using existing resources",
    author = {Pradet, Quentin  and
      Danlos, Laurence  and
      de Chalendar, Ga{\"e}l},
    editor = "Calzolari, Nicoletta  and
      Choukri, Khalid  and
      Declerck, Thierry  and
      Loftsson, Hrafn  and
      Maegaard, Bente  and
      Mariani, Joseph  and
      Moreno, Asuncion  and
      Odijk, Jan  and
      Piperidis, Stelios",
    booktitle = "Proceedings of the Ninth International Conference on Language Resources and Evaluation ({LREC}'14)",
    month = may,
    year = "2014",
    address = "Reykjavik, Iceland",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/L14-1204/",
    pages = "1122--1126",
    abstract = {VerbNet is an English lexical resource for verbs that has proven useful for English NLP due to its high coverage and coherent classification. Such a resource doesn’t exist for other languages, despite some (mostly automatic and unsupervised) attempts. We show how to semi-automatically adapt VerbNet using existing resources designed for di{\"i}{\textlnot}€erent purposes. This study focuses on French and uses two French resources: a semantic lexicon (Les Verbes Fran{\c{c}}ais) and a syntactic lexicon (Lexique-Grammaire).}
}