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Department of Computer Science, TU Kaiserslautern, Gottlieb-Daimler-Straße 47, Kaiserslautern, Germany, German Research Center for Artificial Intelligence (DFKI), Kaiserslautern and Germany

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1 Papers

A Study of Various Text Augmentation Techniques for Relation Classification in Free Text

Topics: Natural Language Processing; Feature Selection and Extraction; Knowledge Acquisition and Representation; Deep Learning and Neural Networks; Classification and Clustering

Authors: Praveen Kumar Badimala Giridhara ¹ , Chinmaya Mishra ¹ , Reddy Kumar Modam Venkataramana ² , Syed Saqib Bukhari ³ and Andreas Dengel ¹

Affiliations: ¹ Department of Computer Science, TU Kaiserslautern, Gottlieb-Daimler-Straße 47, Kaiserslautern, Germany, German Research Center for Artificial Intelligence (DFKI), Kaiserslautern and Germany , ² Department of Computer Science, TU Kaiserslautern, Gottlieb-Daimler-Straße 47, Kaiserslautern and Germany , ³ German Research Center for Artificial Intelligence (DFKI), Kaiserslautern and Germany

Digital Object Identifier: 10.5220/0007311003600367

Proceedings of ICPRAM 2019, ISBN: 978-989-758-351-3

Quick Abstract

Data augmentation techniques have been widely used in visual recognition tasks as it is easy to generate new data by simple and straight forward image transformations. However, when it comes to text data augmentations, it is difficult to find appropriate transformation techniques which also preserve the contextual and grammatical structure of language texts. In this paper, we explore various text data augmentation techniques in text space and word embedding space. We study the effect of various augmented datasets on the efficiency of different deep learning models for relation classification in text.

Related Ontology Subjects/Areas/Topics (10)

Applications	Knowledge Engineering and Ontology Development	Pattern Recognition
Artificial Intelligence	Knowledge-Based Systems	Symbolic Systems
Feature Selection and Extraction	Natural Language Processing	Theory and Methods
Knowledge Acquisition and Representation