Relation classification is an important semantic processing task whose state-of-theart systems still rely on the use of 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 encodes the task of classifying the relationship between two nominals marked in a sentence. Using CR-CNN, we outperform the stateof-the-art for this dataset and achieve a F1 of 84.1 without using any costly handcrafted features. Additionally, our experimental results also evidence 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-ofthe-art results if we consider only the text between the two target nominals.
dos REF addressed the task using Convolutional Neural Network (CNN).
Classifying Relations by Ranking with Convolutional Neural Networks
ArXiv
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Computer Science