An Enhanced Deep Neural Network-Based Architecture for Joint Extraction of Entity Mentions and Relations
Elham Parsaeimehr, Mehdi Fartash, Javad Akbari Torkestani · International Journal of Fuzzy Logic and Intelligent Systems · 2020
Named entity recognition and relation extraction are two principal tasks in most natural language processing systems.The majority of methods used in the field implement these two issues independently, thus leading to possible problems such as error propagation from one component (entity detection) to another (relation extraction).To solve such problems, we propose a new architecture for joint identification of entity mentions and their relation by employing a deep neural network framework.The model not only overcomes the error propagation challenge but also improves the detection results of both tasks owing to the cooperation with each other.Experiments on publicly available sources demonstrate that our joint model surpasses competitors in terms of accuracy.The results highlight the improvement achieved by the proposed deep neural network framework for the entity mention and relation classification tasks.Furthermore, we tested the effect of increasing the sentence length and demonstrated its negative influence on the performance.