Abstract
It is beyond human capabilities to analyze a huge amount of short text produced on the World Wide Web in the form of search queries, social media platforms, etc. Due to many difficulties underlying short text for automated processing, i.e, sparsity and insufficient context, the traditional text classification approaches cannot easily be applied to short text. This study discusses a Convolutional Neural Network (CNN) based approach for short text classification. Given a short text, the model generates the text representation by leveraging words together with the entities. To validate the effectiveness of the model, several experiments have been conducted on different datasets. The results suggest that the proposed model is capable of performing short text classification with a high accuracy and outperforms the baseline.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
- 2.
- 3.
- 4.
- 5.
- 6.
References
Chen, J., Hu, Y., Liu, J., Xiao, Y., Jiang, H.: Deep short text classification with knowledge powered attention. In: AAAI (2019)
Chen, Z., Tang, Y., Zhang, Z., Zhang, C., Wang, L.: Sentiment-aware short text classification based on convolutional neural network and attention. In: IEEE - ICTAI (2019)
Ferragina, P., Scaiella, U.: TAGME: on-the-fly annotation of short text fragments (by Wikipedia entities). In: CIKM (2010)
Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar, October 2014, pp. 1746–1751. Association for Computational Linguistics (2014)
Kowsari, K., Meimandi, K.J., Heidarysafa, M., Mendu, S., Barnes, L.E., Brown, D.E.: Text classification algorithms: a survey. Information 10(4), 150 (2019)
Le, Q.V., Mikolov, T.: Distributed representations of sentences and documents. In: ICML (2014)
Li, X., Roth, D.: Learning question classifiers: the role of semantic information. Nat. Lang. Eng. 12(3), 229–249 (2006)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: NIPS (2013)
Nakov, P., Kozareva, Z., Ritter, A., Rosenthal, S., Stoyanov, V., Wilson, T.: Semeval-2013 task 2: sentiment analysis in Twitter. CoRR, abs/1912.06806 (2019)
Oshikawa, R., Qian, J., Wang, W.Y.: A survey on natural language processing for fake news detection. CoRR, abs/1811.00770 (2018)
Peters, M.E., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., Zettlemoyer, L.: Deep contextualized word representations. In: NAACL-HLT (2018)
Rashkin, H., Choi, E., Jang, J.Y., Volkova, S., Choi, Y.: Truth of varying shades: analyzing language in fake news and political fact-checking. In: EMNLP (2017)
Song, Y., Wang, H., Wang, Z., Li, H., Chen, W.: Short text conceptualization using a probabilistic knowledgebase. In: IJCAI. IJCAI/AAAI (2011)
Wang, J., Wang, Z., Zhang, D., Yan, J.: Combining knowledge with deep convolutional neural networks for short text classification. In: Sierra, C. (ed.) IJCAI (2017)
Wang, Z., Wang, H.: Understanding short texts. In: The Association for Computational Linguistics (ACL) (Tutorial), August 2016
Wu, W., Li, H., Wang, H., Zhu, K.Q.: Probase: a probabilistic taxonomy for text understanding. In: ACM SIGMOD (2012)
Xu, J., Cai, Y.: Incorporating context-relevant knowledge into convolutional neural networks for short text classification. In: AAAI (2019)
Yamada, I., et al.: Wikipedia2Vec: an efficient toolkit for learning and visualizing the embeddings of words and entities from Wikipedia. arXiv preprint 1812.06280v3 (2020)
Zeng, J., Li, J., Song, Y., Gao, C., Lyu, M.R., King, I.: Topic memory networks for short text classification. In: EMNLP (2018)
Zhang, X., LeCun, Y.: Text understanding from scratch. CoRR, abs/1502.01710 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Alam, M., Bie, Q., Türker, R., Sack, H. (2020). Entity-Based Short Text Classification Using Convolutional Neural Networks. In: Keet, C.M., Dumontier, M. (eds) Knowledge Engineering and Knowledge Management. EKAW 2020. Lecture Notes in Computer Science(), vol 12387. Springer, Cham. https://doi.org/10.1007/978-3-030-61244-3_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-61244-3_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-61243-6
Online ISBN: 978-3-030-61244-3
eBook Packages: Computer ScienceComputer Science (R0)