How to Adapt Deep Learning Models to a New Domain: The Case of Biomedical Relation Extraction
| Autor: | Jefferson A. Peña-Torres, Raúl E. Gutiérrez, Víctor A. Bucheli, Fabio A. González |
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| Jazyk: | English<br />Spanish; Castilian |
| Rok vydání: | 2019 |
| Předmět: | |
| Zdroj: | TecnoLógicas, Vol 22, Pp 49-62 (2019) |
| Druh dokumentu: | article |
| ISSN: | 0123-7799 2256-5337 22565337 |
| DOI: | 10.22430/22565337.1483 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
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