@inproceedings{grivaz-2010-human,
    title = "Human Judgements on Causation in {F}rench Texts",
    author = "Grivaz, C{\'e}cile",
    editor = "Calzolari, Nicoletta  and
      Choukri, Khalid  and
      Maegaard, Bente  and
      Mariani, Joseph  and
      Odijk, Jan  and
      Piperidis, Stelios  and
      Rosner, Mike  and
      Tapias, Daniel",
    booktitle = "Proceedings of the Seventh International Conference on Language Resources and Evaluation ({LREC}'10)",
    month = may,
    year = "2010",
    address = "Valletta, Malta",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/ingest-emnlp/L10-1093/",
    abstract = "The annotation of causal relations in natural language texts can lead to a low inter-annotator agreement. A French corpus annotated with causal relations would be helpful for the evaluation of programs that extract causal knowledge, as well as for the study of the expression of causation. As previous theoretical work provides no necessary and sufficient condition that would allow an annotator to easily identify causation, we explore features that are associated with causation in human judgements. We present an experiment that allows us to elicit intuitive features of causation. We test the statistical association of features of causation from theoretical previous work with causation itself in human judgements in an annotation experiment. We then establish guidelines based on these features for annotating a French corpus. We argue that our approach leads to coherent annotation guidelines, since it allows us to obtain a {\ensuremath{\kappa}} = 0.84 agreement between the majority of the annotators answers and our own educated judgements. We present these annotation instructions in detail."
}