The Ability of Word Embeddings to Capture Word Similarities
Journal
International Journal on Natural Language Computing
Date Issued
2020-06-30
Author(s)
Kalajdjieski, Jovan
Stojanovska, Frosina
DOI
10.5121/ijnlc.2020.9302
Abstract
Distributed language representation has become the most widely used technique for language representation in various natural language processing tasks. Most of the natural language processing models that are based on deep learning techniques use already pre-trained distributed word representations, commonly called word embedding. Determining the most qualitative word embedding is of crucial importance for such models. However, selecting the appropriate word embedding is a perplexing task since the projected embedding space is not intuitive to humans.In this paper, we explore different approaches for creating distributed word representations. We perform an intrinsic evaluation of several state-of-the-art word embedding methods. Their performance on capturing word similarities is analysed with existing benchmark datasets for word pairs similarities. The research in this paper conducts a correlation analysis between ground truth word similarities and similarities obtained by different word embedding methods.