@inproceedings{chiang-etal-2020-understanding,
    title = "Understanding the Source of Semantic Regularities in Word Embeddings",
    author = "Chiang, Hsiao-Yu  and
      Camacho-Collados, Jose  and
      Pardos, Zachary",
    editor = "Fern{\'a}ndez, Raquel  and
      Linzen, Tal",
    booktitle = "Proceedings of the 24th Conference on Computational Natural Language Learning",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2020.conll-1.9/",
    doi = "10.18653/v1/2020.conll-1.9",
    pages = "119--131",
    abstract = "Semantic relations are core to how humans understand and express concepts in the real world using language. Recently, there has been a thread of research aimed at modeling these relations by learning vector representations from text corpora. Most of these approaches focus strictly on leveraging the co-occurrences of relationship word pairs within sentences. In this paper, we investigate the hypothesis that examples of a lexical relation in a corpus are fundamental to a neural word embedding{'}s ability to complete analogies involving the relation. Our experiments, in which we remove all known examples of a relation from training corpora, show only marginal degradation in analogy completion performance involving the removed relation. This finding enhances our understanding of neural word embeddings, showing that co-occurrence information of a particular semantic relation is not the main source of their structural regularity."
}