Abstract
Semantic relations are underlying relation between the concepts present in sentences. Semantic relation plays an important role for building applications namely, information retrieval (IR), information extraction (IE), question answering (QA) and chatbots. This paper proposed a model to extract entities, and identifying the semantic relations exists between the entities. This paper is used to extract entities using novel feature extraction techniques with Naïve Bayes approach. Furthermore, it is used to identify the semantic relations using various machine learning approaches. This paper addresses eight semantic relations namely, prevent, no cure, disease only, side effect, vague, treatment only, cure and none. The model is evaluated by standard metrics and produced the result of 68.42% F-score for this data set.
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Srinivasan, R., Subalalitha, C.N. (2021). Semantic Relation from Biomedical Text Documents Using Machine Learning Algorithm. In: Dash, S.S., Panigrahi, B.K., Das, S. (eds) Sixth International Conference on Intelligent Computing and Applications . Advances in Intelligent Systems and Computing, vol 1369. Springer, Singapore. https://doi.org/10.1007/978-981-16-1335-7_30
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DOI: https://doi.org/10.1007/978-981-16-1335-7_30
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