Classifying Relations by Ranking with Convolutional Neural Networks

Cicero Nogueira dos Santos; Bing Xiang; Bowen Zhou

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arXiv · 1504.06580

Classifying Relations by Ranking with Convolutional Neural Networks

Cicero Nogueira dos Santos·Bing Xiang·Bowen Zhou

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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.

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BibTeX RIS

Cicero Nogueira dos Santos, Bing Xiang, Bowen Zhou. 2015-05-24. Classifying Relations by Ranking with Convolutional Neural Networks. https://arxiv.org/abs/1504.06580

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