Semantic Relatedness and Taxonomic Word Embeddings

• Main Authors: Kacmajor, Magdalena, Kelleher, John D, Klubicka, Filip, Maldonado, Alfredo
• Format: Journal Article
• Language: English
• Published: 14-02-2020
• Subjects: Computer Science - Artificial Intelligence; Computer Science - Computation and Language; Computer Science - Learning
• DOI: 10.48550/arxiv.2002.06235

This paper connects a series of papers dealing with taxonomic word
embeddings. It begins by noting that there are different types of semantic
relatedness and that different lexical representations encode different forms
of relatedness. A particularly important distinction within semantic
relatedness is that of thematic versus taxonomic relatedness. Next, we present
a number of experiments that analyse taxonomic embeddings that have been
trained on a synthetic corpus that has been generated via a random walk over a
taxonomy. These experiments demonstrate how the properties of the synthetic
corpus, such as the percentage of rare words, are affected by the shape of the
knowledge graph the corpus is generated from. Finally, we explore the
interactions between the relative sizes of natural and synthetic corpora on the
performance of embeddings when taxonomic and thematic embeddings are combined.