@inproceedings{tanvi-manish-2023-hindi,
    title = "{H}indi Causal {T}ime{B}ank: an Annotated Causal Event Corpus",
    author = "Kamble, Tanvi  and
      Shrivastava, Manish",
    editor = "D. Pawar, Jyoti  and
      Lalitha Devi, Sobha",
    booktitle = "Proceedings of the 20th International Conference on Natural Language Processing (ICON)",
    month = dec,
    year = "2023",
    address = "Goa University, Goa, India",
    publisher = "NLP Association of India (NLPAI)",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2023.icon-1.14/",
    pages = "145--150",
    abstract = "Events and states have gained importance in NLP and information retrieval for being semantically rich temporal and spatial information indicators. Event causality helps us identify which events are necessary for another event to occur. The cause-effect event pairs can be relevant for multiple NLP tasks like question answering, summarization, etc. Multiple efforts have been made to identify causal events in documents but very little work has been done in this field in the Hindi language. We create an annotated corpus for detecting and classifying causal event relations on top of the Hindi Timebank (Goel et al., 2020), the {\textquoteleft}Hindi Causal Timebank' (Hindi CTB). We introduce semantic causal relations like Purpose, Reason, and Enablement inspired from Bejan and Harabagiu (2008)`s annotation scheme and add some special cases particular to Hindi language."
}