Abstract
A common subtask of knowledge acquisition from natural-language texts is classifying words and recognizing entities and actions in the text. It is used in the analysis of both scientific and narrative texts. Thesauri and lexical databases containing hypernymy relationship between synsets may be a useful resource for entity and action recognition. In this study, we compared the performance of three major English thesauri containing hypernymy relationship in different forms - WordNet, Roget’s Thesaurus, and FrameNet - on 6 word-meaning categories that are used for the analysis of narrative and scientific natural-language texts. The results show that WordNet contains more words than FrameNet, and is more suitable for scientific texts, but FrameNet contains better-defined hypernyms and shows better precision for many narrative natural-language tasks, especially for verbs. Roget’s Thesaurus performance is average between WordNet and FrameNet in most word-meaning categories Enhancing FrameNet by adding more lexical units to existing frames would allow creating a powerful resource for entity and action recognition in text analysis. Fixing WordNet problems require revising its system of hypernyms.
The reported study was funded by RFBR, project number 20-07-00764.
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Sychev, O., Kamennov, Y. (2021). Eligibility of English Hypernymy Resources for Extracting Knowledge from Natural-Language Texts. In: Samsonovich, A.V., Gudwin, R.R., Simões, A.d.S. (eds) Brain-Inspired Cognitive Architectures for Artificial Intelligence: BICA*AI 2020. BICA 2020. Advances in Intelligent Systems and Computing, vol 1310. Springer, Cham. https://doi.org/10.1007/978-3-030-65596-9_61
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