@inproceedings{dasigi-etal-2017-ontology,
    title = "Ontology-Aware Token Embeddings for Prepositional Phrase Attachment",
    author = "Dasigi, Pradeep  and
      Ammar, Waleed  and
      Dyer, Chris  and
      Hovy, Eduard",
    editor = "Barzilay, Regina  and
      Kan, Min-Yen",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/P17-1191/",
    doi = "10.18653/v1/P17-1191",
    pages = "2089--2098",
    abstract = "Type-level word embeddings use the same set of parameters to represent all instances of a word regardless of its context, ignoring the inherent lexical ambiguity in language. Instead, we embed semantic concepts (or synsets) as defined in WordNet and represent a word token in a particular context by estimating a distribution over relevant semantic concepts. We use the new, context-sensitive embeddings in a model for predicting prepositional phrase (PP) attachments and jointly learn the concept embeddings and model parameters. We show that using context-sensitive embeddings improves the accuracy of the PP attachment model by 5.4{\%} absolute points, which amounts to a 34.4{\%} relative reduction in errors."
}