@inproceedings{koloski-etal-2022-knowledge,
    title = "Knowledge informed sustainability detection from short financial texts",
    author = "Koloski, Boshko  and
      Montariol, Syrielle  and
      Purver, Matthew  and
      Pollak, Senja",
    editor = "Chen, Chung-Chi  and
      Huang, Hen-Hsen  and
      Takamura, Hiroya  and
      Chen, Hsin-Hsi",
    booktitle = "Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP)",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates (Hybrid)",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.finnlp-1.31",
    doi = "10.18653/v1/2022.finnlp-1.31",
    pages = "228--234",
    abstract = "There is a global trend for responsible investing and the need for developing automated methods for analyzing and Environmental, Social and Governance (ESG) related elements in financial texts is raising. In this work we propose a solution to the FinSim4-ESG task, consisting of binary classification of sentences into sustainable or unsustainable. We propose a novel knowledge-based latent heterogeneous representation that is based on knowledge from taxonomies and knowledge graphs and multiple contemporary document representations. We hypothesize that an approach based on a combination of knowledge and document representations can introduce significant improvement over conventional document representation approaches. We consider ensembles on classifier as well on representation level late-fusion and early fusion. The proposed approaches achieve competitive accuracy of 89 and are 5.85 behind the best achieved score.",
}