Classifying Relations by Ranking with Convolutional Neural Networks
Abstract
Relation classification is an important semantic processing task for which state-ofthe-art systems still rely on costly handcrafted features. In this work we tackle the relation classification task using a convolutional neural network that performs classification by ranking (CR-CNN). We propose a new pairwise ranking loss function that makes it easy to reduce the impact of artificial classes. We perform experiments using the the SemEval-2010 Task 8 dataset, which is designed for the task of classifying the relationship between two nominals marked in a sentence. Using CRCNN, we outperform the state-of-the-art for this dataset and achieve a F1 of 84.1 without using any costly handcrafted features. Additionally, our experimental results show that: (1) our approach is more effective than CNN followed by a softmax classifier; (2) omitting the representation of the artificial class Other improves both precision and recall; and (3) using only word embeddings as input features is enough to achieve state-of-the-art results if we consider only the text between the two target nominals.
Cite
@article{arxiv.1504.06580,
title = {Classifying Relations by Ranking with Convolutional Neural Networks},
author = {Cicero Nogueira dos Santos and Bing Xiang and Bowen Zhou},
journal= {arXiv preprint arXiv:1504.06580},
year = {2015}
}
Comments
Accepted as a long paper in the 53rd Annual Meeting of the Association for Computational Linguistics (ACL 2015)