@inproceedings{mondal-etal-2022-expertneurons,
    title = "{E}xpert{N}eurons at {F}in{C}ausal 2022 Task 2: Causality Extraction for Financial Documents",
    author = "Mondal, Joydeb  and
      Bhat, Nagaraj  and
      Sarkar, Pramir  and
      Reza, Shahid",
    editor = "El-Haj, Mahmoud  and
      Rayson, Paul  and
      Zmandar, Nadhem",
    booktitle = "Proceedings of the 4th Financial Narrative Processing Workshop @LREC2022",
    month = jun,
    year = "2022",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://preview.aclanthology.org/ingest-emnlp/2022.fnp-1.22/",
    pages = "128--130",
    abstract = "In this paper describes the approach which we have built for causality extraction from the financial documents that we have submitted for FinCausal 2022 task 2. We proving a solution with intelligent pre-processing and post-processing to detect the number of cause and effect in a financial document and extract them. Our given approach achieved 90{\%} as F1 score(weighted-average) for the official blind evaluation dataset."
}