Abstract
Event detection is increasingly gaining attention within the fields of natural language processing and social network analysis. Graph models have always been integral to social media analysis literature. Owing to the long processing time and time complexities of graph-based algorithms, these models were initially very difficult to improve upon. Over the past few years, researchers proposed many approaches to create representations such as word2vec and doc2vec [11]. With the emergence of graph embedding techniques in recent years using deep learning techniques such as node2vec, it is possible to extract node embeddings that can be used to embed graph information into machine learning methods. We introduce SnakeGraph, a new model which uses the sequences of words making up each body of text along with key representations such as the user and the date. These representations can help us learn about the main ideas communicated via written language. However, our method not only looks at both the content of text and how it links to other key information, but also factors the relationship between words in our text as they appear in sequence and overlap as they appear across different bodies of text. We believe that date and user embeddings can especially shed light on event detection literature.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bojanowski, P., et al.: Enriching word vectors with subword information. Trans. Assoc. Comput. Linguist. 5, 135–146 (2017)
Cai, H., Zheng, V.W., Chang, K.C.-C.: A comprehensive survey of graph embedding: problems, techniques, and applications. IEEE Trans. Knowl. Data Eng. 30(9), 1616–1637 (2018)
Dhingra, B., et al.: Tweet2Vec: character-based distributed representations for social media. In: The 54th Annual Meeting of the Association for Computational Linguistics (2016)
Feng, X., et al.: A language-independent neural network for event detection. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), vol. 2 (2016)
Ganguly, S., et al.: Author2Vec: learning author representations by combining content and link information. In: Proceedings of the 25th International Conference Companion on World Wide Web. International World Wide Web Conferences Steering Committee (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. ACM (2016)
Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: Advances in Neural Information Processing Systems (2017)
Ifrim, G., Shi, B., Brigadir, I.: Event detection in Twitter using aggressive filtering and hierarchical tweet clustering. In: SNOW-DC@ WWW (2014)
Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2014)
Kumar, S., et al.: Community interaction and conflict on the web. In: Proceedings of The Web Conference (WWW) (2018)
Liu, Y., et al.: Topical word embeddings. In: Twenty-Ninth AAAI Conference on Artificial Intelligence (2015)
Narayanan, A., et al.: Subgraph2Vec: learning distributed representations of rooted sub-graphs from large graphs. arXiv preprint arXiv:1606.08928 (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. ACM (2014)
Qin, Y., et al.: Frame-based representation for event detection on Twitter. IEICE Trans. Inf. Syst. 101(4), 1180–1188 (2018)
Vosoughi, S., Vijayaraghavan, P., Roy, D.: Tweet2Vec: learning tweet embeddings using character-level CNN-LSTM encoder-decoder. In: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval. ACM (2016)
Xu, K., et al.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018)
van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008)
Acknowledgment
This project (No. 117E566) is funded by the Scientific and Technological Research Council of Turkey (TUBITAK).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Genc, H., Yilmaz, B. (2019). Text-Based Event Detection: Deciphering Date Information Using Graph Embeddings. In: Ordonez, C., Song, IY., Anderst-Kotsis, G., Tjoa, A., Khalil, I. (eds) Big Data Analytics and Knowledge Discovery. DaWaK 2019. Lecture Notes in Computer Science(), vol 11708. Springer, Cham. https://doi.org/10.1007/978-3-030-27520-4_19
Download citation
DOI: https://doi.org/10.1007/978-3-030-27520-4_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-27519-8
Online ISBN: 978-3-030-27520-4
eBook Packages: Computer ScienceComputer Science (R0)