LABDA at SemEval-2017 Task 10: Relation Classification between keyphrases via Convolutional Neural Network

Víctor Suárez-Paniagua, Isabel Segura-Bedmar, Paloma Martínez


Abstract
In this paper, we describe our participation at the subtask of extraction of relationships between two identified keyphrases. This task can be very helpful in improving search engines for scientific articles. Our approach is based on the use of a convolutional neural network (CNN) trained on the training dataset. This deep learning model has already achieved successful results for the extraction relationships between named entities. Thus, our hypothesis is that this model can be also applied to extract relations between keyphrases. The official results of the task show that our architecture obtained an F1-score of 0.38% for Keyphrases Relation Classification. This performance is lower than the expected due to the generic preprocessing phase and the basic configuration of the CNN model, more complex architectures are proposed as future work to increase the classification rate.
Anthology ID:
S17-2169
Volume:
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
Month:
August
Year:
2017
Address:
Vancouver, Canada
Venue:
*SEMEVAL
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
969–972
Language:
URL:
https://www.aclweb.org/anthology/S17-2169
DOI:
10.18653/v1/S17-2169
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/S17-2169.pdf