Comparative Analysis of Contextual Relation Extraction based on Deep Learning Models

TL;DR

This paper compares various deep learning models for Contextual Relation Extraction, emphasizing their effectiveness in handling complex sentences with multiple relations, which traditional machine learning models struggle with.

Contribution

It provides a comprehensive analysis of deep learning techniques for relation extraction, highlighting improvements over traditional methods in complex sentence scenarios.

Findings

Deep learning models outperform traditional ML in complex relation extraction.

Hybrid models effectively handle sentences with multiple relations.

Analysis guides future development of CRE systems in biomedical domains.

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.