Abstract
Scientific papers are important for scholars to track trends in specific research areas. With the increase in the number of scientific papers, it is difficult for scholars to read all the papers to extract emerging or noteworthy knowledge. Paper modeling can help scholars master the key information in scientific papers, and relation classification (RC) between entity pairs is a major approach to paper modeling. To the best of our knowledge, most of the state-of-the-art RC methods are using entire sentence’s context information as input. However, long sentences have too much noise information, which is useless for classification. In this paper, a flexible context is selected as the input information for convolution neural network (CNN), which greatly reduces the noise. Moreover, we find that entity type is another important feature for RC. Based on these findings, we construct a typical CNN architecture to learn features from raw texts automatically, and use a softmax function to classify the entity pairs. Our experiment on SemEval-2018 task 7 dataset yields a macro-F1 value of 83.91%, ranking first among all participants.
Z. Yin, S. Wu and Y. Yin have equal contribution to this paper.
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Acknowledgements
Firstly, we would like to thank Bin Mao, Changhai Tian and Yuming Ye for their valuable suggestions on the initial version of this paper, which have helped a lot to improve the paper. Secondly, we want to express gratitudes to the anonymous reviewers for their hard work and kind comments, which will further improve our work in the future. Additionally, this work was supported by the National Natural Science Foundation of China (No. 61602490).
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Yin, Z. et al. (2019). Relation Classification in Scientific Papers Based on Convolutional Neural Network. In: Tang, J., Kan, MY., Zhao, D., Li, S., Zan, H. (eds) Natural Language Processing and Chinese Computing. NLPCC 2019. Lecture Notes in Computer Science(), vol 11839. Springer, Cham. https://doi.org/10.1007/978-3-030-32236-6_21
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