Sidecar: Augmenting Word Embedding Models with Expert Knowledge

  • Mathieu Lemay
  • Daniel ShapiroEmail author
  • Mary Kate MacPherson
  • Kieran Yee
  • Hamza Qassoud
  • Miodrag Bolic
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1130)


This work investigates a method for enriching pre-trained word embeddings with domain-specific information using a small, custom word embedding. For a classification task on text containing out-of-vocabulary expert jargon, this new approach improves the prediction accuracy when using popular models such as Word2Vec (71.5% to 76.6%), GloVe (73.5% to 77.2%), and fastText (75.8% to 79.6%). Furthermore, an analysis of the approach demonstrates that expert knowledge is improved in terms of discrimination and inconsistency. Another advantage of this word embedding augmentation technique is that it is computationally inexpensive and leverages the general syntactic information encoded in large pre-trained word embeddings.


Transfer learning Word embedding Expertise Knowledge Embedding retraining 


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Mathieu Lemay
    • 1
  • Daniel Shapiro
    • 1
    • 2
    Email author
  • Mary Kate MacPherson
    • 1
  • Kieran Yee
    • 1
  • Hamza Qassoud
    • 2
  • Miodrag Bolic
    • 2
  1. Inc.OttawaCanada
  2. 2.School of Electrical Engineering and Computer ScienceUniversity of OttawaOttawaCanada

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