@inproceedings{ratnaparkhi-kumar-2021-resolving,
    title = "Resolving Prepositional Phrase Attachment Ambiguities with Contextualized Word Embeddings",
    author = "Ratnaparkhi, Adwait  and
      Kumar, Atul",
    editor = "Bandyopadhyay, Sivaji  and
      Devi, Sobha Lalitha  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 18th International Conference on Natural Language Processing (ICON)",
    month = dec,
    year = "2021",
    address = "National Institute of Technology Silchar, Silchar, India",
    publisher = "NLP Association of India (NLPAI)",
    url = "https://preview.aclanthology.org/ingest-emnlp/2021.icon-main.40/",
    pages = "335--340",
    abstract = "This paper applies contextualized word embedding models to a long-standing problem in the natural language parsing community, namely prepositional phrase attachment. Following past formulations of this problem, we use data sets in which the attachment decision is both a binary-valued choice as well as a multi-valued choice. We present a deep learning architecture that fine-tunes the output of a contextualized word embedding model for the purpose of predicting attachment decisions. We present experiments on two commonly used datasets that outperform the previous best results, using only the original training data and the unannotated full sentence context."
}