Improving the Relation Classification Using Convolutional Neural Network

• Published in: IOP conference series. Materials Science and Engineering Vol. 1187; no. 1; pp. 12004 - 12009
• Main Authors: Kamath, S, Karibasappa, K G, Reddy, Anvitha, Kallur, Arati M, Priyanka, B B, Bhagya, B P
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.09.2021
• Subjects: Artificial neural networks; Classification; CNN; Deep Learning; Experimentation; Feature extraction; Machine learning; Natural language processing; Neural networks; NLP; Relation extraction; Semantics; Word2vec embedding; Words (language)
• ISSN: 1757-8981; 1757-899X
• DOI: 10.1088/1757-899X/1187/1/012004

Abstract Relation extraction has been the emerging research topic in the field of Natural Language Processing. The proposed work classifies the relations among the data considering the semantic relevance of words using word2vec embeddings towards training the convolutional neural network. We intended to use the semantic relevance of the words in the document to enrich the learning of the embeddings for improved classification. We designed a framework to automatically extract the relations between the entities using deep learning techniques. The framework includes pre-processing, extracting the feature vectors using word2vec embedding, and classification using convolutional neural networks. We perform extensive experimentation using benchmark datasets and show improved classification accuracy in comparison with the state-of-the-art methodologies using appropriate methods and also including the additional relations.