@inproceedings{agarwal-etal-2020-langresearchlab,
    title = "{L}ang{R}esearch{L}ab{\_}{NC} at {F}in{C}ausal 2020, Task 1: A Knowledge Induced Neural Net for Causality Detection",
    author = "Agarwal, Raksha  and
      Verma, Ishaan  and
      Chatterjee, Niladri",
    editor = "El-Haj, Dr Mahmoud  and
      Athanasakou, Dr Vasiliki  and
      Ferradans, Dr Sira  and
      Salzedo, Dr Catherine  and
      Elhag, Dr Ans  and
      Bouamor, Dr Houda  and
      Litvak, Dr Marina  and
      Rayson, Dr Paul  and
      Giannakopoulos, Dr George  and
      Pittaras, Nikiforos",
    booktitle = "Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "COLING",
    url = "https://preview.aclanthology.org/ingest-emnlp/2020.fnp-1.4/",
    pages = "33--39",
    abstract = "Identifying causal relationships in a text is essential for achieving comprehensive natural language understanding. The present work proposes a combination of features derived from pre-trained BERT with linguistic features for training a supervised classifier for the task of Causality Detection. The Linguistic features help to inject knowledge about the semantic and syntactic structure of the input sentences. Experiments on the FinCausal Shared Task1 datasets indicate that the combination of Linguistic features with BERT improves overall performance for causality detection. The proposed system achieves a weighted average F1 score of 0.952 on the post-evaluation dataset."
}