@inproceedings{tagarev-etal-2019-comparison,
    title = "Comparison of Machine Learning Approaches for Industry Classification Based on Textual Descriptions of Companies",
    author = "Tagarev, Andrey  and
      Tulechki, Nikola  and
      Boytcheva, Svetla",
    editor = "Mitkov, Ruslan  and
      Angelova, Galia",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/R19-1134/",
    doi = "10.26615/978-954-452-056-4_134",
    pages = "1169--1175",
    abstract = "This paper addresses the task of categorizing companies within industry classification schemes. The datasets consists of encyclopedic articles about companies and their economic activities. The target classification schema is build by mapping linked open data in a semi-supervised manner. Target classes are build bottom-up from DBpedia. We apply several state of the art text classification techniques, based both on deep-learning and classical vector-space models."
}