Imperial College London
Dr Felipe Orihuela-Espina

DrFelipeOrihuela-Espina

Faculty of Medicine, Department of Surgery & Cancer

Honorary Lecturer
 
 
 
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Publications

Citation

BibTex format

@inproceedings{García-Mendoza:2022:10.1007/978-3-031-19496-2_8,
author = {García-Mendoza, J-L and Villaseñor-Pineda, L and Buscaldi, D and Bustio-Martínez, L and Orihuela-Espina, F},
doi = {10.1007/978-3-031-19496-2_8},
pages = {101--113},
publisher = {Springer Nature Switzerland},
title = {Evaluation of a new representation for noise reduction in Distant Supervision},
url = {http://dx.doi.org/10.1007/978-3-031-19496-2_8},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - Distant Supervision is a relation extraction approach that allows automatic labeling of a dataset. However, this labeling introduces noise in the labels (e.g., when two entities in a sentence are automatically labeled with an invalid relation). Noise in labels makes difficult the relation extraction task. This noise is precisely one of the main challenges of this task. Until now, the methods that incorporate a previous noise reduction step do not evaluate the performance of this step. This paper evaluates the noise reduction using a new representation obtained with autoencoders. In addition, it was incoporated more information to the input of the autoencoder proposed in the state-of-the-art to improve the representation over which the noise is reduced. Also, three methods were proposed to select the instances considered as real. As a result, it was obtained the highest values of the area under the ROC curves using the improved input combined with state-of-the-art anomaly detection methods. Moreover, the three proposed selection methods significantly improve the existing method in the literature.
AU  - García-Mendoza,J-L
AU  - Villaseñor-Pineda,L
AU  - Buscaldi,D
AU  - Bustio-Martínez,L
AU  - Orihuela-Espina,F
DO  - 10.1007/978-3-031-19496-2_8
EP  - 113
PB  - Springer Nature Switzerland
PY  - 2022///
SN  - 0302-9743
SP  - 101
TI  - Evaluation of a new representation for noise reduction in Distant Supervision
UR  - http://dx.doi.org/10.1007/978-3-031-19496-2_8
UR  - https://link.springer.com/chapter/10.1007/978-3-031-19496-2_8
UR  - http://hdl.handle.net/10044/1/104859
ER  -
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