How to Adapt Deep Learning Models to a New Domain: The Case of Biomedical Relation Extraction
Open Access
- 5 December 2019
- journal article
- Published by Instituto Tecnologico Metropolitano (ITM) in TecnoLógicas
- Vol. 22, 49-62
- https://doi.org/10.22430/22565337.1483
Abstract
In this article, we study the relation extraction problem from Natural Language Processing (NLP) implementing a domain adaptation setting without external resources. We trained a Deep Learning (DL) model for Relation Extraction (RE), which extracts semantic relations in the biomedical domain. However, can the model be applied to different domains? The model should be adaptable to automatically extract relationships across different domains using the DL network. Completely training DL models in a short time is impractical because the models should quickly adapt to different datasets in several domains without delay. Therefore, adaptation is crucial for intelligent systems, where changing factors and unanticipated perturbations are common. In this study, we present a detailed analysis of the problem, as well as preliminary experimentation, results, and their evaluation.Keywords
This publication has 32 references indexed in Scilit:
- Relation classification via recurrent neural network with attention and tensor layersBig Data Mining and Analytics, 2018
- Chemical–gene relation extraction using recursive neural networkDatabase: The Journal of Biological Databases and Curation, 2018
- CoTypePublished by Association for Computing Machinery (ACM) ,2017
- Relation Classification: CNN or RNN?Published by Springer Science and Business Media LLC ,2016
- Neural Relation Extraction with Selective Attention over InstancesPublished by Association for Computational Linguistics (ACL) ,2016
- Classifying Relations via Long Short Term Memory Networks along Shortest Dependency PathsPublished by Association for Computational Linguistics (ACL) ,2015
- Representing Text for Joint Embedding of Text and Knowledge BasesPublished by Association for Computational Linguistics (ACL) ,2015
- Review of Relation Extraction Methods: What Is New Out There?Communications in Computer and Information Science, 2014
- A shortest path dependency kernel for relation extractionPublished by Association for Computational Linguistics (ACL) ,2005
- Combining lexical, syntactic, and semantic features with maximum entropy models for extracting relationsPublished by Association for Computational Linguistics (ACL) ,2004