近年來，卷積神經網路不只於影像處理領域中取得重大成果，於自然語言處理相關課題中也有一定成效。本論文提出一個結合語意關鍵詞的卷積神經網路模型，並應用於自然語言處理中的文本分類問題。語意關鍵詞從廣義知網中萃取知識用以建立詞彙意義，將其結果應用於卷積神經網路，獲得具有語意概念的深度學習模型。
本研究於新聞主題偵測領域與讀者情緒分析領域進行實驗，分別使用兩筆資料集各含有將近140,000篇與50,000篇的雅虎（Yahoo）中文新聞。實驗結果顯示本系統於新聞主題偵測資料集優於其他常見的主題模型，如LDA-SVM，而且Macro平均的F1-measure較FBA提升7.5\%；於讀者情緒分析資料集中Macro平均之F1-measure達到90%以上的優異成果，並且比TBA提高將近6%。
根據實驗結果顯示，本論文之模型能夠結合語意關鍵詞與卷積神經網路之優點，使模型能學習人類的知識且準確預測結果。

In recent years, convolutional neural network (CNN) achieved a remarkable success not only in image processing, but also in natural language processing. In this thesis, we proposed a deep learning model combining keyword semantics with convolutional neural network and applied it to text classification in natural language processing. The keyword semantics extracted knowledge from E-HowNet and generated the sense of words. Moreover, these structures are regarded as input of CNNs to acquire a deep learning model with keyword semantics.
We conducted experiments with our model on text classification in two domains, topic detection and reader emotion analysis. There are two corpora for topic detection and reader emotion respectively. Moreover, the experiment results show that our model improves the macro F1-measure by 7.5% compared to several well-known topic models including LDA-SVM and FBA. According to the experiment results, our model combines both benefits of keyword semantics and convolutional nerual network. Thus, allowing the model to accurately predict the result by learning human knowledge.

[1] I. G. Israel, “Information explosion and university libraries : Current trends and strategies for intervention,” vol. 30, 12 2010.
[2] IBM Marketing Cloud, “10 key marketing trends for 2017,” 2017. Available at https://www-01.ibm.com/common/ssi/cgi-bin/ssialias?htmlfid=WRL12345USEN.
[3] F. Sebastiani, “Machine learning in automated text categorization,” ACM Comput. Surv., vol. 34, no. 1, pp. 1–47, 2002.
[4] J. P. Turian, L. Ratinov, and Y. Bengio, “Word representations: A simple and general method for semi-supervised learning,” in ACL 2010, Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, July 11-16, 2010, Uppsala, Sweden, pp. 384–394, 2010.
[5] R. E. Bellman, Dynamic programming. Princeton University Press, 1957. ISBN 978-0-691-07951-6.
[6] G.SaltonandM.McGill,IntroductiontoModernInformationRetrieval. McGraw-Hill Book Company, 1984.
[7] G. E. Hinton, “Learning distributed representations of concepts,” in Proceedings of the eighth annual conference of the cognitive science society, vol. 1, p. 12, Amherst, MA, 1986.
[8] T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient estimation of word representations in vector space,” CoRR, vol. abs/1301.3781, 2013.
[9] A. Mnih and K. Kavukcuoglu, “Learning word embeddings efficiently with noise-contrastive estimation,” in Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013, Lake Tahoe, Nevada, United States., pp. 2265–2273, 2013.
[10] S.Chen,K.Chen,H.Hung,andB.Chen,“Exploring Word Embedding and Concept Information for Language Model Adaptation in Mandarin Large Vocabulary Continuous Speech Recognition,” in Proceedings of the 27th Conference on Computational Linguistics and Speech Processing, ROCLING2015, National Chiao Tung University, Hsinchu, Taiwan, October 1-2, 2015, 2015.
[11] L. Qiu, Y. Cao, Z. Nie, Y. Yu, and Y. Rui, “Learning word representation considering proximity and ambiguity,” in Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, July 27 -31, 2014, Québec City, Québec, Canada., pp. 1572–1578, 2014.
[12] Q. V. Le and T. Mikolov, “Distributed representations of sentences and documents,” in Proceedings of the 31th International Conference on Machine Learning, ICML 2014, Beijing, China, 21-26 June 2014, pp. 1188–1196, 2014.
[13] M. Sasaki and K. Kita, “Rule-based text categorization using hierarchical categories,” in Systems, Man, and Cybernetics, 1998. 1998 IEEE International Conference on, vol. 3, pp. 2827–2830, IEEE, 1998.
[14] T. Yang, Y. Hsieh, Y. Chung, C. Shih, S. Liu, Y. Chang, and W. Hsu, “Principle-Based Approach for Semi-Automatic Construction of a Restaurant Question Answering System from Limited Datasets,” in IRI, pp. 520–524, IEEE Computer Society, 2016.
[15] N. Chang, H. Dai, Y. Hsieh, and W. Hsu, “Statistical principle-based approach for detecting mirna-target gene interaction articles,” in BIBE, pp. 79–86, IEEE Computer Society, 2016.
[16] P.-T. Lai, M.-S. Huang, C.-H. Su, R. T.-H. Tsai, and W.-L. Hsu, “Statistical principle-based approach for gene and protein related object recognition,” in Proceedings of the BioCreative V. 5 Challenge Evaluation Workshop, 2017.
[17] S. E. Robertson and K. S. Jones, “Relevance weighting of search terms,” JASIS, vol. 27, no. 3, pp. 129–146, 1976.
[18] L. S. Larkey and W. B. Croft, “Combining classifiers in text categorization,” in Proceedings of the 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR’96, August18-22, 1996, Zurich, Switzerland (Special Issue of the SIGIR Forum), pp. 289–297, 1996.
[19] M. Sahami, “Learning limited dependence bayesian classifiers,” in Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96), Portland, Oregon, USA, pp. 335–338, 1996.
[20] S. Kim, K. Han, H. Rim, and S. Myaeng, “Some effective techniques for naive bayes text classification,” IEEE Trans. Knowl. Data Eng., vol. 18, no. 11, pp. 1457–1466, 2006.
[21] J. Chen, H. Huang, S. Tian, and Y. Qu, “Feature selection for text classification with naïve bayes,” Expert Syst. Appl., vol. 36, no. 3, pp. 5432–5435, 2009.
[22] M.RogatiandY.Yang, “High-performingfeatureselectionfortextclassification,”in Proceedings of the 2002 ACM CIKM International Conference on Information and Knowledge Management, McLean, VA, USA, November 4-9, 2002, pp. 659–661, 2002.
[23] Y. Zhou, Y. Li, and S. Xia, “An improved KNN text classification algorithm based on clustering,” JCP, vol. 4, no. 3, pp. 230–237, 2009.
[24] I. Batal and M. Hauskrecht, “Boosting KNN text classification accuracy by using supervised term weighting schemes,” in Proceedings of the 18th ACM Conference on Information and Knowledge Management, CIKM 2009, Hong Kong, China, November 2-6, 2009, pp. 2041–2044, 2009.
[25] C. Cortes and V. Vapnik, “Support-vector networks,” Machine Learning, vol. 20, no. 3, pp. 273–297, 1995.
[26] H. Kim, P. Howland, and H. Park, “Dimension reduction in text classification with support vector machines,” Journal of Machine Learning Research, vol. 6, pp. 37–53, 2005.
[27] C. Chu, C. Wang, Y. Chang, Y. Wu, Y. Hsieh, and W. Hsu, “Sentiment analysis on chinese movie review with distributed keyword vector representation,” in Conference on Technologies and Applications of Artificial Intelligence, TAAI 2016, Hsinchu, Taiwan, November 25-27, 2016, pp. 84–89, 2016.
[28] R. Nallapati, “Semantic language models for topic detection and tracking,” in Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics, HLT-NAACL 2003, Edmonton, Canada, May 27 - June 1, 2003, 2003.
[29] D. M. Blei, A. Y. Ng, and M. I. Jordan, “Latent dirichlet allocation,” Journal of Machine Learning Research, vol. 3, pp. 993–1022, 2003.
[30] X. Zhang and T. Wang, “Topic tracking with dynamic topic model and topic-based weighting method,” JSW, vol. 5, no. 5, pp. 482–489, 2010.
[31] Y. Chang, Y. Hsieh, C. Chen, C. Liu, C. Lu, and W. Hsu, “Semantic frame-based statistical approach for topic detection,” in PACLIC, pp. 75–84, The PACLIC 28 Organizing Committee and PACLIC Steering Committee / ACL / Department of Linguistics, Faculty of Arts, Chulalongkorn University, 2014.
[32] Y. Wu, Y. Ding, X. Wang, and J. Xu, “On-line hot topic recommendation using tolerance rough set based topic clustering,” JCP, vol. 5, no. 4, pp. 549–556, 2010.
[33] P. D. Turney, “Thumbs up or thumbs down? semantic orientation applied to unsupervised classification of reviews,” in Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, July 6-12, 2002, Philadelphia, PA, USA., pp. 417–424, 2002.
[34] T. Wilson, J. Wiebe, and P. Hoffmann, “Recognizing contextual polarity: An exploration of features for phrase-level sentiment analysis,” Computational Linguistics, vol. 35, no. 3, pp. 399–433, 2009.
[35] Y.Chen, S.Y.M.Lee, S.Li, andC.Huang, “Emotion cause detection with linguistic constructions,” in COLING 2010, 23rd International Conference on Computational Linguistics, Proceedings of the Conference, 23-27 August 2010, Beijing, China, pp. 179–187, 2010.
[36] M. Purver and S. A. Battersby, “Experimenting with distant supervision for emotion classification,” in EACL 2012, 13th Conference of the European Chapter of the Association for Computational Linguistics, Avignon, France, April 23-27, 2012, pp. 482–491, 2012.
[37] C. Quan and F. Ren, “Construction of a blog emotion corpus for chinese emotional expression analysis,” in Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, EMNLP 2009, 6-7 August 2009, Singapore, A meeting of SIGDAT, a Special Interest Group of the ACL, pp. 1446–1454, 2009.
[38] D. Das and S. Bandyopadhyay, “Word to sentence level emotion tagging for bengali blogs,” in ACL 2009, Proceedings of the 47th Annual Meeting of the Association for Computational Linguistics and the 4th International Joint Conference on Natural Language Processing of the AFNLP, 2-7 August 2009, Singapore, Short Papers, pp. 149–152, 2009.
[39] K. H. Lin, C. Yang, and H. Chen, “Emotion classification of online news articles from the reader’s perspective,” in 2008 IEEE / WIC / ACM International Conference on Web Intelligence, WI 2008, 9-12 December 2008, Sydney, NSW, Australia, Main Conference Proceedings, pp. 220–226, 2008.
[40] Y. Tang and H. Chen, “Mining sentiment words from microblogs for predicting writer-reader emotion transition,” in Proceedings of the Eighth International Conference on Language Resources and Evaluation, LREC 2012, Istanbul, Turkey, May 23-25, 2012, pp. 1226–1229, 2012.
[41] K. H. Lin, C. Yang, and H. Chen, “What emotions do news articles trigger in their readers?,” in SIGIR, pp. 733–734, ACM, 2007.
[42] Y. Chang, C. Chu, C. C. Chen, and W. Hsu, “Linguistic template extraction for recognizing reader-emotion,” IJCLCLP, vol. 21, no. 1, 2016.
[43] O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “ImageNet Large Scale Visual Recognition Challenge,” International Journal of Computer Vision (IJCV), vol. 115, no. 3, pp. 211–252, 2015.
[44] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012, Lake Tahoe, Nevada, United States., pp. 1106–1114, 2012.
[45] G.E.Hinton,S.Osindero,andY.W.Teh,“A fast learning algorithm for deep belief nets,” Neural Computation, vol. 18, no. 7, pp. 1527–1554, 2006.
[46] K. Hornik, M. B. Stinchcombe, and H. White, “Multilayer feedforward networks are universal approximators,” Neural Networks, vol. 2, no. 5, pp. 359–366, 1989.
[47] F. Rosenblatt, “The perceptron: a probabilistic model for information storage and organization in the brain.,” Psychological review, vol. 65, no. 6, p. 386, 1958.
[48] M. Minsky and S. Papert, Perceptrons: An Introduction to Computational Geometry. The MIT Press, 1969.
[49] Y. LeCun, B. E. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. E. Hubbard, and L. D. Jackel, “Backpropagation applied to handwritten zip code recognition,” Neural Computation, vol. 1, no. 4, pp. 541–551, 1989.
[50] C. Olah, “Understanding lstm networks,” 2015. Available at http://colah.github.io/posts/2015-08-Understanding-LSTMs/.
[51] S. Hochreiter, Y. Bengio, P. Frasconi, J. Schmidhuber, et al., “Gradient flow in recurrent nets: the difficulty of learning long-term dependencies,” 2001.
[52] S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Computation, vol. 9, no. 8, pp. 1735–1780, 1997.
[53] I. Sutskever, O. Vinyals, and Q. V. Le, “Sequence to sequence learning with neural networks,” in Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, December 8-13 2014, Montreal, Quebec, Canada, pp. 3104–3112, 2014.
[54] K.Cho, B.vanMerrienboer, Ç.Gülçehre, D.Bahdanau, F.Bougares, H.Schwenk, and Y. Bengio, “Learning phrase representations using RNN encoder-decoder for statistical machine translation,” in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014, October 25-29, 2014, Doha, Qatar, A meeting of SIGDAT, a Special Interest Group of the ACL, pp. 1724–1734, 2014.
[55] D.Bahdanau, K.Cho, andY.Bengio, “Neural machine translation by jointly learning to align and translate,” CoRR, vol. abs/1409.0473, 2014.
[56] J. Gehring, M. Auli, D. Grangier, D. Yarats, and Y. N. Dauphin, “Convolutional sequence to sequence learning,” in Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Austrsalia, 6-11 August 2017, pp. 1243–1252, 2017.
[57] A.Vaswani, N.Shazeer, N.Parmar, J.Uszkoreit, L.Jones, A.N.Gomez, L.Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 4-9 December 2017, Long Beach, CA, USA, pp. 6000–6010, 2017.
[58] H. Huang, C. Zhu, Y. Shen, and W. Chen, “Fusionnet: Fusing via fully-aware attention with application to machine comprehension,” CoRR, vol. abs/1711.07341, 2017.
[59] R. Nallapati, B. Zhou, C. N. dos Santos, Ç. Gülçehre, and B. Xiang, “Abstractive text summarization using sequence-to-sequence rnns and beyond,” in Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning, CoNLL 2016, Berlin, Germany, August 11-12, 2016, pp. 280–290, 2016.
[60] Y. Hsieh, S. Liu, K. Chen, H. Wang, W. Hsu, and B. Chen, “運用序列到序列生成架構於重寫式自動摘要 (exploiting sequence-to-sequence generation framework for automatic abstractive summarization)[in chinese],” in Proceedings of the 28th Conference on Computational Linguistics and Speech Processing, ROCLING 2016, National Cheng Kung University, Tainan, Taiwan, October 6-7, 2015, 2016.
[61] B. Hu, Q. Chen, and F. Zhu, “LCSTS: A large scale chinese short text summarization dataset,” in Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, EMNLP 2015, Lisbon, Portugal, September 17-21, 2015, pp. 1967–1972, 2015.
[62] Y. Hsieh, Y. Chang, Y. Huang, S. Yeh, C. Chen, and W. Hsu, “MONPA: multi-objective named-entity and part-of-speech annotator for chinese using recurrent neural network,” in Proceedings of the Eighth International Joint Conference on Natural Language Processing, IJCNLP 2017, Taipei, Taiwan, November 27 - December 1, 2017, Volume 2: Short Papers, pp. 80–85, 2017.
[63] J. P. C. Chiu and E. Nichols, “Named entity recognition with bidirectional lstm-cnns,” TACL, vol. 4, pp. 357–370, 2016.
[64] E. F. T. K. Sang and F. D. Meulder, “Introduction to the conll-2003 shared task: Language-independent named entity recognition,” in Proceedings of the Seventh Conference on Natural Language Learning, CoNLL 2003, Held in cooperation with HLT-NAACL 2003, Edmonton, Canada, May 31 - June 1, 2003, pp. 142–147, 2003.
[65] M. Iyyer, V. Manjunatha, J. L. Boyd-Graber, and H. D. III, “Deep unordered composition rivals syntactic methods for text classification,”inProceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, ACL 2015, July 26-31, 2015, Beijing, China, Volume 1: Long Papers, pp. 1681–1691, 2015.
[66] M. Ghiassi, M. Olschimke, B. Moon, and P. Arnaudo, “Automated text classification using a dynamic artificial neural network model,” Expert Syst. Appl., vol. 39, no. 12, pp. 10967–10976, 2012.
[67] D. Tang, B. Qin, and T. Liu, “Document modeling with gated recurrent neural network for sentiment classification,” in Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, EMNLP 2015, Lisbon, Portugal, September 17-21, 2015, pp. 1422–1432, 2015.
[68] P. Zhou, Z. Qi, S. Zheng, J. Xu, H. Bao, and B. Xu, “Text classification improved by integrating bidirectional LSTM with two-dimensional max pooling,” in COLING 2016, 26th International Conference on Computational Linguistics, Proceedings of the Conference: Technical Papers, December 11-16, 2016, Osaka, Japan, pp. 3485–3495, 2016.
[69] Y. Zhou, C. Li, B. Xu, J. Xu, J. Cao, and B. Xu, “Hierarchical hybrid attention networks for chinese conversation topic classification,” in Neural Information Processing - 24th International Conference, ICONIP 2017, Guangzhou, China, November 14-18, 2017, Proceedings, Part II, pp. 540–550, 2017.
[70] Y. Kim, “Convolutional neural networks for sentence classification,” in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014, October 25-29, 2014, Doha, Qatar, A meeting of SIGDAT, a Special Interest Group of the ACL, pp. 1746–1751, 2014.
[71] X. Zhang, J. J. Zhao, and Y. LeCun, “Character-level convolutional networks for text classification,” in Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, December 7-12, 2015, Montreal, Quebec, Canada, pp. 649–657, 2015.
[72] R.Johnson and T.Zhang, “Semi-supervised convolutional neural networks for text categorization via region embedding,” in Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, December 7-12, 2015, Montreal, Quebec, Canada, pp. 919–927, 2015.
[73] T. R. Gruber, “A translation approach to portable ontology specifications,” Knowledge acquisition, vol. 5, no. 2, pp. 199–220, 1993.
[74] M.-C. Chung, “A study and construction on ontology-based document classification web service,” Master Thesis, Department of Computer Science and Information Engineering (CSIE), National Cheng Kung University, Tainan City, 2004.
[75] C.-S. Lee, C.-P. Chen, H.-J. Chen, and Y.-H. Kuo, “A fuzzy classification agent for personal e-news service,” International Journal of Fuzzy Systems, vol. 4, no. 4, pp. 849–856, 2002.
[76] Y.-C.Chang,C.-S.Lee,Y.-C.Huang,Y.-H.Kuo,andS.-C.Hsu,“基於ontology之專案監控知識管理之研究,” National University of Tainan, Journal of Scientific and Technological Studies, vol. 40, no. 1, pp. 69–87, 2006.
[77] N. Shadbolt, T. Berners-Lee, and W. Hall, “The semantic web revisited,” IEEE intelligent systems, vol. 21, no. 3, pp. 96–101, 2006.
[78] C. O. Alm, D. Roth, and R. Sproat, “Emotions from text: Machine learning for text-based emotion prediction,” in HLT/EMNLP 2005, Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference, 6-8 October 2005, Vancouver, British Columbia, Canada, pp. 579–586, 2005.
[79] A. Agarwal, B. Xie, I. Vovsha, O. Rambow, and R. Passonneau, “Sentiment analysis of twitter data,” inProceedingsof the workshop on languagesin social media, pp. 30–38, Association for Computational Linguistics, 2011.
[80] P.-Y. Chang, “Automatic ontology construction by using a chinese parser and a lexical knowledge base,” Master Thesis, Department of Electrical Engineering, National Chung Cheng University, Chiayi County, 2009.
[81] C.-S. Sung, “詞網同義詞集的中文語意表達之研究,” Master Thesis, Department of Computer Science, Soochow University, Taipei City, 2003.
[82] S.-Y. Chen, “Using e-hownet and wikipedia in advertisement recommendation for scripts,” Master Thesis, Department of Computer Science and Information Engineering (CSIE), National Taiwan Normal University, Taipei City, 2016.
[83] C. Li, C. Yu, and H. Chen, “廣義知網詞彙意見極性的預測 (predicting the semantic orientation of terms in e-hownet) [in chinese],” in Proceedings of the 23rd Conference on Computational Linguistics and Speech Processing, ROCLING 2011, Taipei, Taiwan, September 8-9, 2011, pp. 151–165, 2011.
[84] C. Lee, Z. Jian, and L. Huang, “A fuzzy ontology and its application to news summarization,” IEEE Trans. Systems, Man, and Cybernetics, Part B, vol. 35, no. 5, pp. 859–880, 2005.
[85] M.Wang,C.Lee,K.Hsieh,C.Hsu,G.Acampora,andC.Chang,“Ontology-based multi-agents for intelligent healthcare applications,” J. Ambient Intelligence and Humanized Computing, vol. 1, no. 2, pp. 111–131, 2010.
[86] Y. Chang, Y. Hsieh, C. Chen, and W. Hsu, “A semantic frame-based intelligent agent for topic detection,” Soft Comput., vol. 21, no. 2, pp. 391–401, 2017.
[87] C. Shih, Y. Hsieh, and W. Hsu, “Sense decomposition from e-hownet for word similarity measurement,” in Proceedings of the 15th IEEE International Conference on Information Reuse and Integration, IRI 2014, Redwood City, CA, USA, August 13-15, 2014, pp. 613–618, 2014.
[88] D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” CoRR, vol. abs/1412.6980, 2014.
[89] F.Nie, H.Huang, X.Cai, andC.H.Q.Ding, “Efficient and robust feature selection via joint 𝑚2, 1-norms minimization,” in NIPS, pp. 1813–1821, Curran Associates, Inc., 2010.
[90] E. Ahmed, M. J. Jones, and T. K. Marks, “An improved deep learning architecture for person re-identification,” in CVPR, pp. 3908–3916, IEEE Computer Society, 2015.
[91] G. E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Improving neural networks by preventing co-adaptation of feature detectors,” CoRR, vol. abs/1207.0580, 2012.
[92] N. Srivastava, G. E. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,” Journal of Machine Learning Research, vol. 15, no. 1, pp. 1929–1958, 2014.
[93] C. J. van Rijsbergen, Information Retrieval. Butterworth, 1979.
[94] C. D. Manning and H. Schütze, Foundations of statistical natural language processing. MIT Press, 2001.
[95] P. Chaovalit and L. Zhou, “Movie review mining: a comparison between supervised and unsupervised classification approaches,” in HICSS, IEEE Computer Society, 2005.
[96] A. Go, R. Bhayani, and L. Huang, “Twitter sentiment classification using distant supervision,” CS224N Project Report, Stanford, vol. 1, no. 12, 2009.
[97] H. Jang, S. Kim, and T. Lam, “Kaggle competitions: Author identification & statoil/c-core iceberg classifier challenge,”