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Evaluation of taxonomic and neural embedding methods for calculating semantic similarity

Published online by Cambridge University Press:  28 September 2021

Dongqiang Yang Open the ORCID record for Dongqiang Yang [Opens in a new window]  and
Yanqin Yin
Dongqiang Yang*
Affiliation:
School of Computer Science and Technology, Shandong Jianzhu University, Jinan, China
Yanqin Yin
Affiliation:
School of Computer Science and Technology, Shandong Jianzhu University, Jinan, China
*
*Corresponding author. E-mail: [email protected]
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Abstract

Modelling semantic similarity plays a fundamental role in lexical semantic applications. A natural way of calculating semantic similarity is to access handcrafted semantic networks, but similarity prediction can also be anticipated in a distributional vector space. Similarity calculation continues to be a challenging task, even with the latest breakthroughs in deep neural language models. We first examined popular methodologies in measuring taxonomic similarity, including edge-counting that solely employs semantic relations in a taxonomy, as well as the complex methods that estimate concept specificity. We further extrapolated three weighting factors in modelling taxonomic similarity. To study the distinct mechanisms between taxonomic and distributional similarity measures, we ran head-to-head comparisons of each measure with human similarity judgements from the perspectives of word frequency, polysemy degree and similarity intensity. Our findings suggest that without fine-tuning the uniform distance, taxonomic similarity measures can depend on the shortest path length as a prime factor to predict semantic similarity; in contrast to distributional semantics, edge-counting is free from sense distribution bias in use and can measure word similarity both literally and metaphorically; the synergy of retrofitting neural embeddings with concept relations in similarity prediction may indicate a new trend to leverage knowledge bases on transfer learning. It appears that a large gap still exists on computing semantic similarity among different ranges of word frequency, polysemous degree and similarity intensity.

Keywords

Semantic SimilarityLexical SemanticsDistributional SemanticsNeural Embeddings
Type
Article
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Natural Language Engineering , Volume 28 , Issue 6 , November 2022 , pp. 733 - 761
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© The Author(s), 2021. Published by Cambridge University Press

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