Abstract
It is believed that language is the window into the mind, thus a fundamental building brick for the development of artificial intelligence. Understanding natural language is therefore a crucial task for computer systems of the future, and as such it is widely studied. An important tool for the decoding of language are semantic similarity measures, or the description of closeness of the meaning of words. A lot of research on semantic similarity on a conceptual level exists, comparing thus objects and their interconnections, which is typical of similarity between nouns. In this article we propose a novel way of exploring spectral similarity, or the closeness of adjectives and adverbs with respect to the spectrum of all words describing the same feature (e.g., the closeness of sometimes and seldom with respect to the spectrum of words going from never to always). The proposed semantic similarity measure is based on overlaps in second order synonyms of words, and its accuracy is validated in this article thanks to a comparison with human assessment of similarity between words. With a custom accuracy estimate taking in account the fuzziness of the proposed similarity measure, an accuracy of the algorithm of about \(96\%\) is obtained.
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Acknowledgements
We thank Jhonny Pincay Nieves and Minh Tue Nguyen for their precious contribution in the review of the data used for the creation of the questionnaire as well as the refinement of the survey itself, and Sara D’Onofrio for her valuable revision of this article. We moreover express our gratitude to all the participants to the survey for their fundamental contribution to the evaluation of our algorithm.
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Colombo, M., Portmann, E. (2021). Semantic Similarity Between Adjectives and Adverbs—The Introduction of a New Measure. In: Kreinovich, V., Hoang Phuong, N. (eds) Soft Computing for Biomedical Applications and Related Topics. Studies in Computational Intelligence, vol 899. Springer, Cham. https://doi.org/10.1007/978-3-030-49536-7_10
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