2022
DOI: 10.3389/frai.2022.780385
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Componential Analysis of English Verbs

Abstract: Computational lexical resources such as WordNet, PropBank, VerbNet, and FrameNet are in regular use in various NLP applications, assisting in the never-ending quest for richer, more precise semantic representations. Coherent class-based organization of lexical units in VerbNet and FrameNet can improve the efficiency of processing by clustering similar items together and sharing descriptions. However, class members are sometimes quite different, and the clustering in both can gloss over useful fine-grained sema… Show more

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Cited by 3 publications
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“…In agreement with Lyons, Chaer (1994: 320) states, "This meaning component analysis may be employed to detect discrepancies from synonymous forms." The componential analysis of semantic is an appropriate analytical method to use in this study, as it can provide details and break down each verb based on its components (Kazeminejad et al, 2022;R, Yayuk and Sawardi, 2022;Wijayanti and Diani, 2022). According to Leech (as adapted from Partana, 2003, p. 123), word meaning analysis is usually perceived as a way of breaking down the significance of a word into its essential characteristics, specifically into components that are in contrast with other components.…”
Section: Introductionmentioning
confidence: 99%
“…In agreement with Lyons, Chaer (1994: 320) states, "This meaning component analysis may be employed to detect discrepancies from synonymous forms." The componential analysis of semantic is an appropriate analytical method to use in this study, as it can provide details and break down each verb based on its components (Kazeminejad et al, 2022;R, Yayuk and Sawardi, 2022;Wijayanti and Diani, 2022). According to Leech (as adapted from Partana, 2003, p. 123), word meaning analysis is usually perceived as a way of breaking down the significance of a word into its essential characteristics, specifically into components that are in contrast with other components.…”
Section: Introductionmentioning
confidence: 99%