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.

翻译：上下文关系抽取(CRE)主要用于借助本体构建知识图谱，执行语义搜索、问答和文本蕴含等多种任务。关系抽取能够从原始文本中识别实体及其之间的关联关系。在生物医学领域，高效且精准的CRE系统对构建领域知识至关重要。现有的机器学习与自然语言处理技术难以高效地从包含两个以上关系及未指定实体的句子中预测复杂关系。本研究采用深度学习技术，基于多句语境识别合适的语义关系。尽管已有多种机器学习模型用于关系抽取，但这些模型仅在处理二元关系（即句中恰好存在于两个实体之间的关系）时表现较好。对于包含多义词的复杂句子，机器学习模型并不适用。为解决上述问题，本文采用混合深度学习模型有效提取复杂句子中的关系。本研究系统分析了多种用于关系抽取的深度学习模型。