Evaluation of taxonomic and neural embedding methods for calculating semantic similarity

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

Verb similarity on the taxonomy of WordNet

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Semantic Distance in WordNet: A Simplified and Improved Measure of Semantic Relatedness

Measures of semantic distance have received a great deal of attention recently in the field of computational lexical semantics. Although techniques for approximating the semantic distance of two concepts have existed for several decades, the introduction of the WordNet lexical database and improvements in corpus analysis have enabled significant improvements in semantic distance measures. In this study we investigate a special kind of semantic distance, called semantic relatedness. Lexical semantic relatedness measures have proved to be useful for a number of applications, such as word sense disambiguation and real-word spelling error correction. Most relatedness measures rely on the observation that the shortest path between nodes in a semantic network provides a representation of the relationship between two concepts. The strength of relatedness is computed in terms of this path. This dissertation makes several significant contributions to the study of semantic relatedness. We describe a new measure that calculates semantic relatedness as a function of the shortest path in a semantic network. The proposed measure achieves better results than other standard measures and yet is much simpler than previous models. The proposed measure is shown to achieve a correlation of r = 0. 897 with the judgments of human test subjects using a standard benchmark data set, representing the best performance reported in the literature. We also provide a general formal description for a class of semantic distance measures — namely, those measures that compute semantic distance from the shortest path in a semantic network. Lastly, we suggest a new methodology for developing path-based semantic distance measures that would limit the possibility of unnecessary complexity in future measures