@inproceedings{grishin-2018-igevorse,
    title = "Igevorse at {S}em{E}val-2018 Task 10: Exploring an Impact of Word Embeddings Concatenation for Capturing Discriminative Attributes",
    author = "Grishin, Maxim",
    editor = "Apidianaki, Marianna  and
      Mohammad, Saif M.  and
      May, Jonathan  and
      Shutova, Ekaterina  and
      Bethard, Steven  and
      Carpuat, Marine",
    booktitle = "Proceedings of the 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/S18-1164/",
    doi = "10.18653/v1/S18-1164",
    pages = "995--998",
    abstract = "This paper presents a comparison of several approaches for capturing discriminative attributes and considers an impact of concatenation of several word embeddings of different nature on the classification performance. A similarity-based method is proposed and compared with classical machine learning approaches. It is shown that this method outperforms others on all the considered word vector models and there is a performance increase when concatenated datasets are used."
}