ETH-DS3Lab at SemEval-2018 Task 7: Effectively Combining Recurrent and Convolutional Neural Networks for Relation Classification and Extraction
This work addresses the problem of knowledge extraction for natural language processing, but it is incremental as it combines existing neural network types for a specific competition.
The paper tackled relation classification and extraction in unstructured text by developing an ensemble of convolutional and recurrent neural networks, achieving first place in 3 out of 4 subtasks at SemEval 2018 Task 7.
Reliably detecting relevant relations between entities in unstructured text is a valuable resource for knowledge extraction, which is why it has awaken significant interest in the field of Natural Language Processing. In this paper, we present a system for relation classification and extraction based on an ensemble of convolutional and recurrent neural networks that ranked first in 3 out of the 4 subtasks at SemEval 2018 Task 7. We provide detailed explanations and grounds for the design choices behind the most relevant features and analyze their importance.