R. Priyadharshini, G. Jeyakodi, P. Shanthi Bala Research Cell: An International Journal of Engineering Science, Special Issue March 2023, Vol. 35, A Peer reviewed and refreed journal, UGC Approved Journal (S.No.63019) (till May 2018) ISSN: 2229-6913(Print), ISSN: 2320-0332(Online), Web Presence: http://ijoe.vidyapublications.com © 2023 Vidya Publications 75 Comparative Analysis of Contextual Relation Extraction based on Deep Learning Models 1 R. Priyadharshini, 2 G. Jeyakodi, 3 P. Shanthi Bala Department of Computer Science, School of Engineering and Technology, Pondicherry University, Puducherry, India 1 priyadharsini.r02@gmail.com, 2 rjeyakodi02@gmail.com, 3 shanthibala.cs@gmail.com ABSTRACT Contextual Relation Extraction (CRE) is mainly used for constructing a knowledge graph with a help of ontology. It performs various tasks such as semantic search, query answering, and textual entailment. Relation extraction identifies the entities from raw texts and the relations among them. An efficient and accurate CRE system is essential for creating domain knowledge in the biomedical industry. Existing Machine Learning and Natural Language Processing (NLP) techniques are not suitable to predict complex relations from sentences that consist of more than two relations and unspecified entities efficiently. In this work, deep learning techniques have been used to identify the appropriate semantic relation based on the context from multiple sentences. Even though various machine learning models have been used for relation extraction, they provide better results only for binary relations, i.e., relations occurred exactly between the two entities in a sentence. Machine learning models are not suited for complex sentences that consist of the words that have various meanings. To address these issues, hybrid deep learning models have been used to extract the relations from complex sentence effectively. This paper explores the analysis of various deep learning models that are used for relation extraction. KEYWORDS: Contextual Relation Extraction, Word Embeddings, BERT, Deep Learning Model. INTRODUCTION Contextual Relation Extraction (CRE) helps to understand the meaning of the entities and their relationship in a sentence. It can improve the performance of Natural Language Processing tasks such as information retrieval, question answering, and semantic search [1]. Named Entity Recognition aims to automatically identify and classify objects like people, products, organizations, locations, etc. The process of identifying the terms in a text and arranging in an appropriate group is a source for named entity recognition and a key component for text analysis. The analysis of common syntactic patterns is an important factor of NER. Many deep learning models solve entity recognition applications such as indexing documents, finding relationship among entities, and building an ontology [2-4]. The combination of NER and CRE can provide a rich understanding of the text by identifying both the entities and their relationships based on the context. The joint modeling of entity recognition and relation classification