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    Word Re-Embedding via Manifold Dimensionality Retention


    Hasan, Souleiman and Curry, Edward (2017) Word Re-Embedding via Manifold Dimensionality Retention. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics (ACL), Stroudsburg, PA, USA, pp. 321-326. ISBN 978-1-945626-83-8

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    Abstract

    Word embeddings seek to recover a Euclidean metric space by mapping words into vectors, starting from words co-occurrences in a corpus. Word embeddings may underestimate the similarity between nearby words, and overestimate it between distant words in the Euclidean metric space. In this paper, we re-embed pre-trained word embeddings with a stage of manifold learning which retains dimensionality. We show that this approach is theoretically founded in the metric recovery paradigm, and empirically show that it can improve on state-of-the-art embeddings in word similarity tasks 0.5 - 5.0% points depending on the original space.

    Item Type: Book Section
    Additional Information: This paper was presented at EMNLP 2017 The Conference on Empirical Methods in Natural Language Processing, September 9-11, 2017 Copenhagen, Denmark.
    Keywords: Embeddings; Set theory; Topology; Vector spaces; Co-occurrence; Euclidean metrics; Manifold learning; On state; Word similarity; Natural language processing systems;
    Academic Unit: Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Faculty of Social Sciences > School of Business
    Item ID: 11995
    Identification Number: https://doi.org/10.18653/v1/D17-1033
    Depositing User: Souleiman Hasan
    Date Deposited: 05 Dec 2019 14:23
    Publisher: Association for Computational Linguistics (ACL)
    Refereed: Yes
    URI:

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