@inproceedings{mamou-etal-2019-multi,
    title = "Multi-Context Term Embeddings: the Use Case of Corpus-based Term Set Expansion",
    author = "Mamou, Jonathan  and
      Pereg, Oren  and
      Wasserblat, Moshe  and
      Dagan, Ido",
    editor = "Rogers, Anna  and
      Drozd, Aleksandr  and
      Rumshisky, Anna  and
      Goldberg, Yoav",
    booktitle = "Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for {NLP}",
    month = jun,
    year = "2019",
    address = "Minneapolis, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/W19-2013/",
    doi = "10.18653/v1/W19-2013",
    pages = "95--101",
    abstract = "In this paper, we present a novel algorithm that combines multi-context term embeddings using a neural classifier and we test this approach on the use case of corpus-based term set expansion. In addition, we present a novel and unique dataset for intrinsic evaluation of corpus-based term set expansion algorithms. We show that, over this dataset, our algorithm provides up to 5 mean average precision points over the best baseline."
}