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k-NN Embedding Stability for word2vec Hyper-Parametrisation in Scientific Text

  • Amna DridiEmail author
  • Mohamed Medhat Gaber
  • R. Muhammad Atif Azad
  • Jagdev Bhogal
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11198)

Abstract

Word embeddings are increasingly attracting the attention of researchers dealing with semantic similarity and analogy tasks. However, finding the optimal hyper-parameters remains an important challenge due to the resulting impact on the revealed analogies mainly for domain-specific corpora. While analogies are highly used for hypotheses synthesis, it is crucial to optimise word embedding hyper-parameters for precise hypothesis synthesis. Therefore, we propose, in this paper, a methodological approach for tuning word embedding hyper-parameters by using the stability of k-nearest neighbors of word vectors within scientific corpora and more specifically Computer Science corpora with Machine learning adopted as a case study. This approach is tested on a dataset created from NIPS (Conference on Neural Information Processing Systems) publications, and evaluated with a curated ACM hierarchy and Wikipedia Machine Learning outline as the gold standard. Our quantitative and qualitative analysis indicate that our approach not only reliably captures interesting patterns like “unsupervised_learning is to kmeans as supervised_learning is to knn”, but also captures the analogical hierarchy structure of Machine Learning and consistently outperforms the \(61\%\) sate-of-the-art embeddings on syntactic accuracy with \(68\%\).

Keywords

Word embedding Word2vec Skip-gram Hyper-parameters k-NN stability ACM hierarchy Wikipedia outline NIPS 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Amna Dridi
    • 1
    Email author
  • Mohamed Medhat Gaber
    • 1
  • R. Muhammad Atif Azad
    • 1
  • Jagdev Bhogal
    • 1
  1. 1.School of Computing and Digital TechnologyBirmingham City UniversityBirminghamUnited Kingdom

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