Abstract
Network Representation Learning (NRL) can learn a latent space representation of each vertex in a topology network structure to reflect linked information. Recently, NRL algorithms have been applied to obtain document embedding in linked document network, such as citation websites. However, most existing document representation methods with NRL are unsupervised and they cannot combine NRL with a concrete task-specific NLP tasks. So in this paper, we propose a unified end-to-end hybrid Linked Document Classification (LDC) model which can capture semantic features and topological structure of documents to improve the performance of document classification. In addition, we investigate to use a more flexible strategy to capture structure similarity to improve the traditional rigid extraction of linked document topology structure. The experimental results suggest that our proposed model outperforms other document classification methods especially in the case of having less training sets.
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References
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Cai, D., Zhang, D., Zhai, C.: Topic modeling with network regularization. In: Proceedings of the 17th International Conference on World Wide Web, pp. 101–110. ACM, China (2008)
Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)
Ganguly, S., Gupta, M., Varma, V., Pudi, V.: Author2Vec: learning author representations by combining content and link information. In: International Conference Companion on World Wide Web. International World Wide Web Conferences Steering Committee, pp. 49–50 (2016)
Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864. ACM (2016)
Kalchbrenner, N., Grefenstette, E., Blunsom, P.: A convolutional neural network for modelling sentences. arXiv preprint arXiv:1404.2188 (2014)
Landauer, T.K., Foltz, P.W., Laham, D.: An introduction to latent semantic analysis. Discourse Process. 25(2–3), 259–284 (1998)
Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: International Conference on Machine Learning, pp. 1188–1196 (2014)
Li, J., Ritter, A., Jurafsky, D.: Learning multi-faceted representations of individuals from heterogeneous evidence using neural networks. arXiv preprint arXiv:1510.05198 (2015)
Li, J., Zhu, J., Zhang, B.: Discriminative deep random walk for network classification. Meeting of the Association for Computational Linguistics, pp. 1004–1013 (2016)
Massa, P., Avesani, P.: Trust-aware recommender systems. In: Proceedings of the 2007 ACM Conference on Recommender systems, pp. 17–24. ACM (2007)
Mei, Q. Ma, H., Lyu, M.R., King, I.: Learning to recommend with trust and distrust relationships. In: RecSys, pp. 189–196. ACM (2009)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
Nakagawa, T., Inui, K., Kurohashi, S.: Dependency tree-based sentiment classification using CRFs with hidden variables. In: Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pp. 786–794. Association for Computational Linguistics (2010)
Pan, S., Wu, J., Zhu, X., Zhang, C., Wang, Y.: Tri-party deep network representation. In: International Joint Conference on Artificial Intelligence, pp. 1895–1901. AAAI Press (2016)
Perozzi, B., Al-Rfou, R., Skiena, S.: Deepwalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 701–710. ACM (2014)
Pennington, J., Socher, R., Manning, C.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. EMNLP, pp. 1532–1543 (2014)
Prabowo, R., Thelwall, M.: Sentiment analysis: a combined approach. J. Inf. 3(2), 143–157 (2009)
Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Inf. Process. Manag. 24(5), 513–523 (1988)
Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE Trans. Signal Process. 45(11), 2673–2681 (1997)
Smilkov, D., Thorat, N., Nicholson, C., Reif, E., Viégas, F.B., Wattenberg, M.: Embedding Projector: interactive visualization and interpretation of embeddings. arXiv preprint arXiv:1611.05469 (2016)
Sun, X., Guo, J., Ding, X., Liu, T.: A general framework for content-enhanced network representation learning. arXiv preprint arXiv:1610.02906 (2016)
Tang, J., Liu, H.: Feature selection with linked data in social media. In: Proceedings of the 2012 SIAM International Conference on Data Mining, pp. 118–128. Society for Industrial and Applied Mathematics (2012)
Tang, J., Qu, M., Mei, Q.: PTE: predictive text embedding through large-scale heterogeneous text networks. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1165–1174. ACM (2015)
Trappey, A.J., Hsu, F.C., Trappey, C.V., Lin, C.I.: Development of a patent document classification and search platform using a back-propagation network. Expert Syst. Appl. 31(4), 755–765 (2006)
Tu, C., Liu, H., Liu, Z., Sun, M.: CANE: context-aware network embedding for relation modeling. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, vol. 1, pp. 1722–1731 (2017)
Yang, C., Liu, Z., Zhao, D., Sun, M., Chang, E.Y.: Network representation learning with rich text information. In: International Conference on Artificial Intelligence, pp. 2111–2117. AAAI Press (2015)
Wang, S., Tang, J., Aggarwal, C., Liu, H.: Linked document embedding for classification. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, pp. 115–124. ACM (2016)
Zhang, D., Yin, J., Zhu, X., Zhang, C.: Network representation learning: a survey. arXiv preprint arXiv:1801.05852 (2017)
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Zhang, Y., Zhang, L., Liu, Y. (2018). Linked Document Classification by Network Representation Learning. In: Sun, M., Liu, T., Wang, X., Liu, Z., Liu, Y. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. CCL NLP-NABD 2018 2018. Lecture Notes in Computer Science(), vol 11221. Springer, Cham. https://doi.org/10.1007/978-3-030-01716-3_25
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DOI: https://doi.org/10.1007/978-3-030-01716-3_25
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