Measuring of similarity between verb pairs in the biomedical domain
An ontology-based information content perspective
Keywords:
semantic, similarity, verb similarity, wordnetAbstract
Finding the similarity Ontology-based between two verbs in bio-medical ontology is a difficult task as there is no standard dataset available. This paper is focused on verb-based similarity. So, the similarity between two nouns is taken as the benchmark for working on the verb similarity. There is no exact idea that the verb hierarchy of wordnet is capable to calculate the verb similarity between two verbs. The finding of similarity considers three parameters such as path, link, and depth. But in this paper, in addition to the path, link, and depth parameters, we also considered parameters such as stem-similarity weighting, derivation nouns weighting, and gloss similarity weighting. Moreover, we implemented two algorithms namely Rich Hierarchy Exploration and Shallow Hierarchy Exploration on a dataset and found that there is no significant difference in its performance.
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References
Fellbaum, Christiane. "A semantic network of English verbs." WordNet: An electronic lexical database 3 (1998): 153-178.
Gupta, A., & Goyal, D. K. K. (2019). Classification of Semantic Similarity Technique between Word Pairs using Word Net. International Journal of Engineering and Advanced Technology, 9(2), 4397–4402. https://doi.org/10.35940/ijeat.b2961.129219
Hirst, G. (2012). Lexical chains as representations of context for the detection and correction of malapropisms.
December.
Jiang, J. J., & Conrath, D. W. (1997). Semantic similarity based on corpus statistics and lexical taxonomy.
Proceedings of the 10th Research on Computational Linguistics International Conference, ROCLING 1997, Rocling X, 19–33.
Leacock, C. (2018). Combining Local Context and WordNet Similarity for Word Sense Identification. WordNet, January 1998. https://doi.org/10.7551/mitpress/7287.003.0018
Lesk, M. (1986). Automatic sense disambiguation using machine readable dictionaries: How to tell a pine cone from an ice cream cone. Proceedings of the 5th Annual International Conference on Systems Documentation, SIGDOC 1986, 24–26. https://doi.org/10.1145/318723.318728
Lin, D. (1997). Using syntactic dependency as local context to resolve word sense ambiguity. i, 64–71. https://doi.org/10.3115/979617.979626
Pedersen, T., & Banerjee, S. (2005). Maximizing Semantic Relatedness to Perform Word Sense Disambiguation.
University of Minnesota Supercomputing Institute Research Report UMSI, 25(June).
Quillian, M. R. (1967). Word concepts: a theory and simulation of some basic semantic capabilities. Behavioral Science, 12(5), 410–430. https://doi.org/10.1002/bs.3830120511
Resnik, Philip, and M. D. (200 C.E.). Measuring verb similarity. 22nd Annual Meeting of the Cognitive Science Society, 546, 2–9.
Resnik, P. (1995). Using Information Content to Evaluate Semantic Similarity in a Taxonomy. 1. http://arxiv.org/abs/cmp-lg/9511007
Resnik, P., & Diab, M. (2000). Measuring verb similarity. Proceedings of the 22nd Annual Meeting of the Cognitive Science Society, 399–404.
Rubenstein, H., & Goodenough, J. B. (1965). Contextual correlates of synonymy. Communications of the ACM, 8(10), 627–633. https://doi.org/10.1145/365628.365657
Schütze, H. (1992). Dimensions of meaning. Proceedings of the International Conference on Supercomputing, Part F1297, 787–796. https://doi.org/10.1109/superc.1992.236684
Yang, D., & Powers, D. M. W. (2005a). Verb similarity on the taxonomy of WordNet. GWC 2006: 3rd International Global WordNet Conference, Proceedings, 121–128.
Yang, D., & Powers, D. M. W. (2005b). Verb similarity on the taxonomy of WordNet. GWC 2006: 3rd International Global WordNet Conference, Proceedings, May 2014, 121–128.
Thanh Ngoc Dao, Troy Simpson, 2005: Measuring similarity between sentences.
Widana, I.K., Dewi, G.A.O.C., Suryasa, W. (2020). Ergonomics approach to improve student concentration on learning process of professional ethics. Journal of Advanced Research in Dynamical and Control Systems, 12(7), 429-445.
Widana, I.K., Sumetri, N.W., Sutapa, I.K., Suryasa, W. (2021). Anthropometric measures for better cardiovascular and musculoskeletal health. Computer Applications in Engineering Education, 29(3), 550–561. https://doi.org/10.1002/cae.22202
Mukhtar, A. U. S., Budu, B., Sanusi B, Y., Mappawere, N. A., & Azniah, A. (2022). The effect of reproductive health education with multimedia video learning on the improvement of fluor albus prevention behavior young woman pathologist. International Journal of Health & Medical Sciences, 5(1), 75-79. https://doi.org/10.21744/ijhms.v5n1.1841
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