Abstract
Word sense disambiguation, Machine Translation (MT), and part-of-speech tagging are just a few of the Natural Language Processing (NLP) applications where Large-scale lexical resources are required. These resources are regarded as treasure in such system applications. As a result, it is critical that these sources include accurate information. These lexical resources for Indian languages have already been generated digitally and are being constructed on a vast scale known as WordNet. WordNet perhaps seems to be the lexical resource that may be applied for a wide range of NLP applications, from digital dictionaries to automated machine translation. This article depicts Gujarati WordNet semantic relations as an acyclic graph. It also illustrates the process of creating links between Gujarati synsets and their English equivalents, and vice versa.
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Patel, M., Joshi, B.K. (2023). GujAGra: An Acyclic Graph to Unify Semantic Knowledge, Antonyms, and Gujarati–English Translation of Input Text. In: Singh, P., Singh, D., Tiwari, V., Misra, S. (eds) Machine Learning and Computational Intelligence Techniques for Data Engineering. MISP 2022. Lecture Notes in Electrical Engineering, vol 998. Springer, Singapore. https://doi.org/10.1007/978-981-99-0047-3_14
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