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.
Santos et al. REF proposed a ranking CNN model, which is trained by a pairwise ranking loss function.
Classifying Relations by Ranking with Convolutional Neural Networks
ArXiv
ArXiv
Computer Science