Abstract
This paper puts forward theme analysis problem in order to automatically solve composition writing questions in Chinese college entrance examination. Theme analysis is to distillate the embedded semantic information from the given materials or documents. We proposes a hierarchical neural network framework to address this problem. Two deep learning based models under the proposed framework are presented. Besides, two transfer learning strategies based on the proposed deep learning models are tried to deal with the lack of large training data for composition theme analysis problems. Experimental results on two tag recommendation data sets show the effect of the proposed deep learning based theme analysis models. Also, we show the effect of the proposed model with transfer learning on a composition writing questions data set built by ourself.
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Acknowledgment
We would like to thank the anonymous reviewers for their thorough reviewing and proposing thoughtful comments to improve our paper. This work was supported by the National 863 Leading Technology Research Project via grant 2015AA015407, Key Projects of National Natural Science Foundation of China via grant 61632011.
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Liu, J., Sun, C., Qin, B. (2017). Deep Learning Based Document Theme Analysis for Composition Generation. In: Sun, M., Wang, X., Chang, B., Xiong, D. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. NLP-NABD CCL 2017 2017. Lecture Notes in Computer Science(), vol 10565. Springer, Cham. https://doi.org/10.1007/978-3-319-69005-6_28
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DOI: https://doi.org/10.1007/978-3-319-69005-6_28
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