Summary of the paper

Title Undersampling Improves Hypernymy Prototypicality Learning
Authors Koki Washio and Tsuneaki Kato
Abstract This paper focuses on supervised hypernymy detection using distributional representations for unknown word pairs. Levy et al. (2015) demonstrated that supervised hypernymy detection suffers from overfitting hypernyms in training data. We show that the problem of overfitting on this task is caused by a characteristic of datasets, which stems from the inherent structure of the language resources used, hierarchical thesauri. The simple data preprocessing method proposed in this paper alleviates this problem. To be more precise, we demonstrate through experiments that the problem that hypernymy classifiers overfit hypernyms in training data comes from a skewed word frequency distribution brought by the quasi-tree structure of a thesaurus, which is a major resource of lexical semantic relation data, and propose a simple undersampling method based on word frequencies that can effectively alleviate overfitting and improve distributional prototypicality learning for unknown word pairs.
Topics Ontologies, Semantics, Lexicon, Lexical Database
Full paper Undersampling Improves Hypernymy Prototypicality Learning
Bibtex @InProceedings{WASHIO18.119,
  author = {Koki Washio and Tsuneaki Kato},
  title = "{Undersampling Improves Hypernymy Prototypicality Learning}",
  booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)},
  year = {2018},
  month = {May 7-12, 2018},
  address = {Miyazaki, Japan},
  editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga},
  publisher = {European Language Resources Association (ELRA)},
  isbn = {979-10-95546-00-9},
  language = {english}
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