Open AccessImproving the Relation Classification Using Convolutional Neural Network
Author(s) -
Shilpa Kamath,
K. Karibasappa,
A. Sai Bharadwaj Reddy,
Arati M. Kallur,
B. Priyanka,
B. P. Bhagya
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1187/1/012004
Subject(s) - word2vec , computer science , artificial intelligence , convolutional neural network , relevance (law) , relation (database) , benchmark (surveying) , relationship extraction , field (mathematics) , deep learning , embedding , machine learning , feature (linguistics) , artificial neural network , natural language processing , data mining , information extraction , linguistics , philosophy , mathematics , geodesy , political science , pure mathematics , law , geography
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.