Abstract
Uncovering a concept hierarchy from short texts, such as tweets and instant messages, is critical for helping users quickly understand the main concepts and sub-concepts in large volumes of such texts. However, due to the sparsity of short texts, existing hierarchical models fail to learn the structural relations among concepts and discover the data more deeply. To solve this problem, we introduce a new notion called context coherence. Context coherence reflects the coverage of a word in a collection of short texts. This coverage is measured by analyzing the relations of words in whole texts. The major advantage of context coherence is that it aligns with the requirements of a concept hierarchy and can lead to a meaningful structure. Moreover, we propose a novel non-parametric context coherence-based model (CCM) that can discover the concept hierarchy from short texts without a pre-defended hierarchy depth and width. We evaluate our model on two real-world datasets. The quantitative evaluations confirm the high quality of the concept hierarchy discovered by our model compared with those of state-of-the-art methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Almars, A., Li, X., Zhao, X., Ibrahim, I.A., Yuan, W., Li, B.: Structured sentiment analysis. In: Advanced Data Mining and Applications (2017)
Blei, D.M., Griffiths, T.L., Jordan, M.I.: The nested Chinese restaurant process and bayesian nonparametric inference of topic hierarchies. J. ACM 57, 7 (2010)
Chen, P., Zhang, N.L., Liu, T., Poon, L.K.M., Chen, Z., Khawar, F.: Latent tree models for hierarchical topic detection. Artif. Intell. 250, 105–124 (2017). https://doi.org/10.1016/j.artint.2017.06.004
Church, K.W., Hanks, P.: Word association norms, mutual information, and lexicography. Comput. Linguist 16, 22–29 (1990)
Gerani, S., Carenini, G., Ng, R.T.: Modeling content and structure for abstractive review summarization. Comput. Speech Lang. 2016, 7 (2016)
Kim, J.H., Kim, D., Kim, S., Oh, A.: Modeling topic hierarchies with the recursive Chinese restaurant process. In: Proceedings of the 21st ACM international conference on Information and knowledge management (2012)
Kim, S., Zhang, J., Chen, Z., Oh, A.H., Liu, S.: A hierarchical aspect-sentiment model for online reviews. In: AAAI (2013)
Kontopoulos, E., Berberidis, C., Dergiades, T., Bassiliades, N.: Ontology-based sentiment analysis of twitter posts. Expert. Syst. Appl. 40, 4065–4074 (2013)
Li, W., McCallum, A.: Pachinko allocation: dag-structured mixture models of topic correlations. In: ICML 2006 (2006)
Mimno, D., Li, W., McCallum, A.: Mixtures of hierarchical topics with pachinko allocation. In: ICML 2007 (2007)
Mimno, D., Wallach, H.M., Talley, E., Leenders, M., McCallum, A.: Optimizing semantic coherence in topic models. In: EMNLP 2011 (2011)
Teh, Y.W., Jordan, M.I., Beal, M.J., Blei, D.M.: Sharing clusters among related groups: hierarchical dirichlet processes. In: Advances in Neural Information Processing Systems, vol. 17 (2005)
Wang, C., Danilevsky, M., Liu, J., Desai, N., Ji, H., Han, J.: Constructing topical hierarchies in heterogeneous information networks. In: ICDM 2013 (2013)
Wang, C., Liu, X., Song, Y., Han, J.: Scalable and robust construction of topical hierarchies. ArXiv e-prints (2014)
Wang, C., et al.: A phrase mining framework for recursive construction of a topical hierarchy. In: KDD 2013 (2013)
Wang, C., Liu, X., Song, Y., Han, J.: Towards interactive construction of topical hierarchy: a recursive tensor decomposition approach. In: KDD 2015 (2015)
Xu, Y., Yin, J., Huang, J., Yin, Y.: Hierarchical topic modeling with automatic knowledge mining. Expert. Syst. Appl. 103, 106-117 (2018)
Yao, L., Mimno, D., McCallum, A.: Efficient methods for topic model inference on streaming document collections. In: KDD 2009 (2009)
Zhao, P., Li, X., Wang, K.: Feature extraction from micro-blogs for comparison of products and services. In: WISE (2013)
Zuo, Y., et al.: Topic modeling of short texts: a pseudo-document view. In: KDD 2016 (2016)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Almars, A., Li, X., Ibrahim, I.A., Zhao, X. (2018). Learning Concept Hierarchy from Short Texts Using Context Coherence. In: Hacid, H., Cellary, W., Wang, H., Paik, HY., Zhou, R. (eds) Web Information Systems Engineering – WISE 2018. WISE 2018. Lecture Notes in Computer Science(), vol 11233. Springer, Cham. https://doi.org/10.1007/978-3-030-02922-7_22
Download citation
DOI: https://doi.org/10.1007/978-3-030-02922-7_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-02921-0
Online ISBN: 978-3-030-02922-7
eBook Packages: Computer ScienceComputer Science (R0)