Abstract
Extracting representative topics and improving the extraction performance is rather challenging. In this work, we formulate a novel problem, called Interactive Area Topics Extraction, and propose a learning interactive topics extraction (LITE) model to regard this problem as a sequential decision making process and construct an end-to-end framework to use interaction with users. In particular, we use recurrent neural network (RNN) decoder to address the problem and policy gradient method to tune the model parameters considering user feedback. Experimental result has shown the effectiveness of the proposed framework.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Al-Zaidy, R.A., Giles, C.L.: Extracting semantic relations for scholarly knowledge base construction. In: Proceedings of 12th IEEE International Conference on Semantic Computing, pp. 56–63 (2018)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Carlson, A., Betteridge, J., Kisiel, B., Settles, B., Hruschka Jr., E.R., Mitchell, T.M.: Toward an architecture for never-ending language learning. In: Proceedings of 24th AAAI Conference on Artificial Intelligence, pp. 1306–1313 (2010)
Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Proceedings of 2014 Conference on Empirical Methods in Natural Language Processing, pp. 1724–1734 (2014)
Deldjoo, Y., Frà, C., Valla, M., Cremonesi, P.: Letting users assist what to watch: an interactive query-by-example movie recommendation system. In: Proceedings of 8th Italian Information Retrieval Workshop, pp. 63–66 (2017)
Hasan, K.S., Ng, V.: Automatic keyphrase extraction: a survey of the state of the art. In: Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics, pp. 1262–1273 (2014)
Jiang, X., Hu, Y., Li, H.: A ranking approach to keyphrase extraction. In: Proceedings of 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 756–757 (2009)
Kohavi, R., Longbotham, R., Sommerfield, D., Henne, R.M.: Controlled experiments on the web: survey and practical guide. Data Min. Knowl. Discov. 18(1), 140–181 (2009)
Lau, J.H., Grieser, K., Newman, D., Baldwin, T.: Automatic labelling of topic models. In: Proceedings of 49th Annual Meeting of the Association for Computational Linguistics, pp. 1536–1545 (2011)
Liang, J., Zhang, Y., Xiao, Y., Wang, H., Wang, W., Zhu, P.: On the transitivity of hypernym-hyponym relations in data-driven lexical taxonomies. In: Proceedings of 31st AAAI Conference on Artificial Intelligence, pp. 1185–1191 (2017)
Mei, Q., Shen, X., Zhai, C.: Automatic labeling of multinomial topic models. In: Proceedings of 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 490–499 (2007)
Mihalcea, R., Tarau, P.: Textrank: Bringing order into text. In: Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing (2004)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Proceedings of 27th Annual Conference on Neural Information Processing Systems, pp. 3111–3119 (2013)
Ponzetto, S.P., Strube, M.: Wikitaxonomy: a large scale knowledge resource. In: Proceedings of 18th European Conference on Artificial Intelligence, pp. 751–752 (2008)
Ruder, S.: An overview of gradient descent optimization algorithms. CoRR abs/1609.04747 (2016)
Samet, H.: The Design and Analysis of Spatial Data Structures. Addison-Wesley, Boston (1990)
Sutton, R.S., McAllester, D.A., Singh, S.P., Mansour, Y.: Policy gradient methods for reinforcement learning with function approximation. In: Proceedings of 1999 Annual Conference on Neural Information Processing Systems, pp. 1057–1063 (1999)
Wang, C., Fan, Y., He, X., Zhou, A.: Predicting hypernym-hyponym relations for Chinese taxonomy learning. Knowl. Inf. Syst. 1–26 (2018, in press)
Yang, Y., Tang, J.: Beyond query: interactive user intention understanding. In: Proceedings of 2015 IEEE International Conference on Data Mining, pp. 519–528 (2015)
Zhang, F., Wang, X., Han, J., Wang, S.: Fast top-k area topics extraction with knowledge base. In: Proceedings of 2018 IEEE International Conference on Data Science in Cyberspace (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Han, J., Rong, W., Zhang, F., Zhang, Y., Tang, J., Xiong, Z. (2018). Interactive Area Topics Extraction with Policy Gradient. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11141. Springer, Cham. https://doi.org/10.1007/978-3-030-01424-7_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-01424-7_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-01423-0
Online ISBN: 978-3-030-01424-7
eBook Packages: Computer ScienceComputer Science (R0)