@inproceedings{al-ghezi-kurimo-2020-graph,
    title = "Graph-based Syntactic Word Embeddings",
    author = "Al-Ghezi, Ragheb  and
      Kurimo, Mikko",
    editor = "Ustalov, Dmitry  and
      Somasundaran, Swapna  and
      Panchenko, Alexander  and
      Malliaros, Fragkiskos D.  and
      Hulpuș, Ioana  and
      Jansen, Peter  and
      Jana, Abhik",
    booktitle = "Proceedings of the Graph-based Methods for Natural Language Processing (TextGraphs)",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Add-Cong-Liu-Florida-Atlantic-University-author-id/2020.textgraphs-1.8/",
    doi = "10.18653/v1/2020.textgraphs-1.8",
    pages = "72--78",
    abstract = "We propose a simple and efficient framework to learn syntactic embeddings based on information derived from constituency parse trees. Using biased random walk methods, our embeddings not only encode syntactic information about words, but they also capture contextual information. We also propose a method to train the embeddings on multiple constituency parse trees to ensure the encoding of global syntactic representation. Quantitative evaluation of the embeddings show a competitive performance on POS tagging task when compared to other types of embeddings, and qualitative evaluation reveals interesting facts about the syntactic typology learned by these embeddings."
}