Enriching Word Embeddings with Global Information and Testing on Highly Inflected Language

Authors

  • Lukáš Svoboda University of West Bohemia, Faculty of Applied Sciences, Department of Computer Science and Engineering
  • Tomáš Brychcín University of West Bohemia, Faculty of Applied Sciences, NTIS—New Technologies for the Information Society

DOI:

https://doi.org/10.13053/cys-23-3-3268

Keywords:

Word, embeddings, global information

Abstract

In this paper we evaluate our new approach based on the Continuous Bag-of-Words and Skip-gram models enriched with global context information on highly inflected Czech language and compare it with English results. As a source of information we use Wikipedia, where articles are organized in a hierarchy of categories. These categories provide useful topical information about each article.Both models are evaluated on standard word similarity and word analogy datasets. Proposed models outperform other word representation methods when similar size of training data is used. Model provide similar performance especially with methods trained on much larger datasets.

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Published

2019-09-25

Issue

Vol. 23 No. 3 (2019): Thematic issue: Computational Linguistics (Guest Editor A. Gelbukh)

Section

Articles of the Thematic Issue

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