[Objective] This paper proposed a model to extract semantic features from texts more comprehensively and to improve the representation of semantics by text vectors. [Methods] We obtained the word-granularity, topic-granularity and character-granularity feature vectors with the help of convolutional neural networks. Then, the three feature vectors were combined by the “merging gate” mechanism to generate the final text vectors. Finally, we examined the model with text classification experiment. [Results] The accuracy (92.56%), the precision (92.33%), the recall (92.07%) and the F-score (92.20%), were 2.40%, 2.05%, 1.77% and 1.91% higher than the results of Text-CNN. [Limitations] The Long-distance dependency features need to be included and the corpus size needs to be expanded. [Conclusions] The proposed model could better represent the text semantics.
( Xu Yanhua, Miao Yujie, Miao Lin , et al. Generating HSK Writing Essays with LDA Model[J]. Data Analysis and Knowledge Discovery, 2018,2(9):80-87.)
[8]
Kim Y, Shim K . TWILITE: A Recommendation System for Twitter Using a Probabilistic Model Based on Latent Dirichlet Allocation[J]. Information Systems, 2014,42:59-77.
[9]
Mikolov T, Sutskever I, Chen K, et al. Distributed Representations of Words and Phrases and Their Compositionality [C]// Proceedings of the Neural Information Processing Systems 2013. 2013.
[10]
Mikolov T, Chen K, Corrado G , et al. Efficient Estimation of Word Representations in Vector Space[OL]. arXiv Preprint, arXiv: 1301.3781.
[11]
Tang D, Qin B, Liu T . Aspect Level Sentiment Classification with Deep Memory Network[OL]. arXiv Preprint, arXiv: 1605.08900.
( Du Hui, Xu Xueke, Wu Dayong , et al. A Sentiment Classification Method Based on Sentiment-Specific Word Embedding[J]. Journal of Chinese Information Processing, 2017,31(3):170-176.)
( Li Xinlei, Wang Hao, Liu Xiaomin , et al. Comparing Text Vector Generators for Weibo Short Text Classification[J]. Data Analysis and Knowledge Discovery, 2018,2(8):41-50.)
[14]
LeCun Y, Bengio Y . Convolutional Networks for Images, Speech, and Time Series[J]. The Handbook of Brain Theory and Neural Networks, 1995: 3361.
[15]
Deng L, Liu Y . Deep Learning in Natural Language Processing[M]. Singapore: Springer Singapore, 2018: 226-229.
[16]
Collobert R, Weston J, Bottou L , et al. Natural Language Processing (Almost) from Scratch[J]. Journal of Machine Learning Research, 2011,12:2493-2537.
[17]
Lei T, Barzilay R, Jaakkola T. Molding CNNs for Text: Non-linear, Non-consecutive Convolutions [C]// Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Lisbon, Portugal. 2015.
[18]
Zhang Y, Roller S, Wallace B C. MGNC-CNN: A Simple Approach to Exploiting Multiple Word Embeddings for Sentence Classification [C]// Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, San Diego, California, USA. Stroudsburg, Pennsylvania, USA: Association for Computational Linguistics, 2016: 1522-1527.
[19]
Yin W, Kann K, Yu M , et al. Comparative Study of CNN and RNN for Natural Language Processing[OL]. arXiv Preprint, arXiv: 1702.01923.
[20]
Dos Santos C, Gatti M. Deep Convolutional Neural Networks for Sentiment Analysis of Short Texts [C]// Proceedings of the 25th International Conference on Computational Linguistics, Dublin, Ireland. Dublin, Ireland: Dublin City University and Association for Computational Linguistics, 2014: 69-78.
[21]
Zhang X, Zhao J, LeCun Y . Character-Level Convolutional Networks for Text Classification [C]// Proceedings of the 2015 Neural Information Processing Systems. 2015.
( Yu Bengong, Zhang Lianbin . Chinese Short Text Classification Based on CP-CNN[J]. Application Research of Computers, 2018,35(4):1001-1004.)
[23]
Zheng X, Chen H, Xu T. Deep Learning for Chinese Word Segmentation and POS Tagging [C]// Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, Seattle, Washington, USA. Stroudsburg, Pennsylvania, USA: Association for Computational Linguistics, 2013: 647-657.
( Wang Yi, Xie Juan, Cheng Ying . Deep Neural Networks Language Model Based on CNN and LSTM Hybrid Architecture[J]. Journal of the China Society for Scientific and Technical Information, 2018,37(2):194-205.)
[25]
Cho K, Van Merrienboer B, Gulcehre C , et al. Learning Phrase Representations Using RNN Encoder-Decoder for Statistical Machine Translation[OL]. arXiv Preprint, arXiv: 1406.1078.
[26]
Wang C, Zhang M, Ma S, et al. Automatic Online News Issue Construction in Web Environment [C]// Proceedings of the 17th International Conference on World Wide Web, Beijing, China. New York, USA: Association for Computational Linguistics, 2008: 457-466.
( “Jieba” Chinese Text Segmentation: Built to be the Best Python Chinese Word Segmentation Module[EB/OL]. (2017-08-28). [2018-12-25]. https://pypi.org/project/jieba/.)
( Chinese Data Preprocessing Material[EB/OL]. [2018-12-25].https://github.com/foowaa/Chinese_from_dongxiexidian.)
[29]
Pedregosa F, Varoquaux G, Gramfort A , et al. Scikit-Learn: Machine Learning in Python[J]. Journal of Machine Learning Research, 2011,12:2825-2830.
[30]
Phan X H, Nguyen C T . GibbsLDA++: A C/C++ Implementation of Latent Dirichlet Allocation (LDA)[EB/OL]. [ 2018- 12- 25]. http://gibbslda.sourceforge.net/.
[31]
Řehůřek R, Sojka P. Software Framework for Topic Modelling with Large Corpora [C]// Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks, Valletta, Malta. Luxembourg: European Language Resources Association, 2010: 45-50.
[32]
Abadi M, Agarwal A, Barham P , et al. TensorFlow: Large-scale Machine Learning on Heterogeneous Systems[EB/OL]. [2018-12-25].https://www.usenix.org/system/files/conference/osdi16/osdi16-abadi.pdf.