@inproceedings{69f8a7ab432d46889dba6d6591042d28,
title = "Deep learning based document theme analysis for composition generation",
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.",
keywords = "Deep learning, Theme analysis, Transfer learning",
author = "Jiahao Liu and Chengjie Sun and Bing Qin",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 16th China National Conference on Computational Linguistics, CCL 2017 and 5th International Symposium on Natural Language Processing Based on Naturally Annotated Big Data, NLP-NABD 2017 ; Conference date: 13-10-2017 Through 15-10-2017",
year = "2017",
doi = "10.1007/978-3-319-69005-6\_28",
language = "英语",
isbn = "9783319690049",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "333--342",
editor = "Maosong Sun and Baobao Chang and Xiaojie Wang and Deyi Xiong",
booktitle = "Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data - 16th China National Conference, CCL 2017 and 5th International Symposium, NLP-NABD 2017, Proceedings",
address = "德国",
}