Abstract
Semantic relations exist two concepts present in the text. Semantic relation extraction becomes an essential part of building an efficient Natural Language Processing (NLP) applications such as Question Answering (QA) and Information Retrieval (IR) system. Automatic semantic relation extraction from text increases the efficiency of these systems by aiding in retrieving more accurate information to the user query. In this research work, we have proposed a framework that extracts agricultural entities and finds the semantic relation exist between entities. Entity extraction is done using a Parts Of Speech (POS) tagger, Word Suffixes and Thesaurus without using any of the external domain-specific knowledge bases, such as Ontology and WordNet. Semantic relation exists between entities are done by using Multinomial Naïve Bayes (MNB) classifier. This paper extracts two entities, namely disease and treatment and focuses on two semantic relations namely “Cure” and “Prevent”. The “Cure” semantic relation expresses the remedial measure for the diseases that prevail in the crops, and the “Prevent” semantic relation shows the precautionary measures that could prevent the crop from being affected. The proposed approach has been trained with 2281 sentences and tested against 553 sentences and then evaluated using standard metrics.
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A Appendix
The term NER was introduced in 1996 at the Message Understanding Conference to refer the entities [19]. NER is defined as to identify and classify the information elements called Named Entities [20]. Biomedical Named Entity Recognition (BNER) is used to determine the biological entities such as protein names, genes, disease name in biomedical texts [9, 21]. Please note that the first paragraph of a section or subsection is not indented. The first paragraph that follows a table, figure, equation etc. does not need an indent, either.
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Srinivasan, R., Subalalitha, C.N. (2020). A Thesaurus Based Semantic Relation Extraction for Agricultural Corpora. In: Chandrabose, A., Furbach, U., Ghosh, A., Kumar M., A. (eds) Computational Intelligence in Data Science. ICCIDS 2020. IFIP Advances in Information and Communication Technology, vol 578. Springer, Cham. https://doi.org/10.1007/978-3-030-63467-4_8
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