Abstract
The present paper reports on the results of the automatic interpretation of Chinese nominal compounds using CNN-Highway network model combined with semantic features. Chinese nominal compound interpretation is aimed to identify semantic relations between verbal nouns like “
” (data acquisition and processing), and “
” (wastewater treatment). The main idea is to define a set of semantic relations of verbal nouns and use deep neural network classifier with semantic features to automatically assign semantic relations to nominal compounds. Experiment shows that our model achieves 84% F1-score on the test dataset. Convolutional layer plus highway network combined with semantic features architecture can effectively solve the problem of Chinese nominal compound interpretation.
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Acknowledgments
This research was funded by the National Natural Science Foundation of China (No.61872402), the Humanities and Social Science Planning (No.17YJAZH068) supported by the Ministry of Education and the Graduate Innovation Fund (No.18YCX008) supported by Beijing Language and Culture University. We hereby express our sincere thanks.
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Li, H., Shao, Y., Li, Y. (2018). Automatic Chinese Nominal Compound Interpretation Based on Deep Neural Networks Combined with Semantic Features. In: Hong, JF., Su, Q., Wu, JS. (eds) Chinese Lexical Semantics. CLSW 2018. Lecture Notes in Computer Science(), vol 11173. Springer, Cham. https://doi.org/10.1007/978-3-030-04015-4_52
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