ICTACT Journal on Soft Computing

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Relation Extraction using Deep Learning Methods-A Survey


Affiliations
1 Department of Computer Science and Engineering, Government Engineering College Sreekrishnapuram, India
2 Department of Computer Science and Engineering, College of Engineering Trivandrum, India
     

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Relation extraction has an important role in extracting structured information from unstructured raw text. This task is a crucial ingredient in numerous information extraction systems seeking to mine structured facts from text. Nowadays, neural networks play an important role in the task of relation extraction. The traditional non deep learning models require feature engineering. Deep Learning models such as Convolutional Neural Networks and Long Short Term Memory networks require less feature engineering than non-deep learning models. Relation Extraction has the potential of employing deep learning models with the creation of huge datasets using distant supervision. This paper surveys the current trend in Relation Extraction using Deep Learning models.

Keywords

Relation Extraction, Deep Learning, LSTM, CNN, Word Embeddings.
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  • Relation Extraction using Deep Learning Methods-A Survey

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Authors

C. A. Deepa
Department of Computer Science and Engineering, Government Engineering College Sreekrishnapuram, India
P. C. ReghuRaj
Department of Computer Science and Engineering, Government Engineering College Sreekrishnapuram, India
Ajeesh Ramanujan
Department of Computer Science and Engineering, College of Engineering Trivandrum, India

Abstract


Relation extraction has an important role in extracting structured information from unstructured raw text. This task is a crucial ingredient in numerous information extraction systems seeking to mine structured facts from text. Nowadays, neural networks play an important role in the task of relation extraction. The traditional non deep learning models require feature engineering. Deep Learning models such as Convolutional Neural Networks and Long Short Term Memory networks require less feature engineering than non-deep learning models. Relation Extraction has the potential of employing deep learning models with the creation of huge datasets using distant supervision. This paper surveys the current trend in Relation Extraction using Deep Learning models.

Keywords


Relation Extraction, Deep Learning, LSTM, CNN, Word Embeddings.

References