Advertisement

5W1H-Based Semantic Segmentation of Tweets for Event Detection Using BERT

  • Kunal ChakmaEmail author
  • Steve Durairaj Swamy
  • Amitava Das
  • Swapan Debbarma
Conference paper
  • 44 Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 1240)

Abstract

Detection of events from Twitter has been one of the significant areas in the Text Mining domain due to the volume of content generated by online users. Twitter is considered as one of the top sources for disseminating information to the users. Due to the short length of texts on Twitter, the content generated is often noisy, which makes the detection of events very difficult. Though research on Twitter event detection has been in existence, most of them focused on implementing statistical measures rather than exploiting the semantics. The work presented in this paper presents an approach for the semantic segmentation of Twitter texts (tweets) by adopting the concept of 5W1H (Who, What, When, Where, Why and How). 5W1H represent the semantic constituents (subject, object and modifiers) of a sentence and the actions of verbs on them. The relationship between a verb and the semantic constituents of a sentence forms the basis for representation of an event. The basic approach of the proposed system is to segment the tweets based on the 5W1H contextual word embeddings generated with the help of recent state-of-the-art technology and then clustering the tweets for the representation of possible events. We compared our approach with a simple baseline system that does not segment the tweets. We evaluated the performance of both the approaches by measuring the cosine similarity of the tweets under a cluster. Our 5W1H segmentation approach produced a similarity score above 82% for the most similar tweets in a cluster against the baseline system that scored below 70%.

Keywords

5W1H Semantic Role Labeling Twitter event detection BERT 

References

  1. 1.
    Kwak, H., Lee, C., Park, H., Moon, S.: What is Twitter, a social network or a news media? In: WWW, pp. 591–600. ACM, Raleigh (2010)Google Scholar
  2. 2.
    James, A.: Topic Detection and Tracking: Event-Based Information Organization, 1st edn. Springer, Boston (2002).  https://doi.org/10.1007/978-1-4615-0933-2CrossRefzbMATHGoogle Scholar
  3. 3.
    Becker, H., Naaman, M., Gravano, L.: Beyond trending topics: real-world event identification on Twitter. In: Proceedings of the Fifth International Conference on Weblogs and Social Media, Barcelona, Catalonia, Spain, 17–21 July 2011 (2011)Google Scholar
  4. 4.
    Dou, W., Wang, K., Ribarsky, W., et al.: Event detection in social media data. In: Proceedings of the IEEE VisWeek Workshop on Interactive Visual Text Analytics - Task Driven Analytics of Social Media Content, pp. 971–980 (2012)Google Scholar
  5. 5.
    Hasan, M., Orgun, M.A., Schwitter, R.: A survey on real-time event detection from the Twitter data stream. Inf. Sci. 44(4), 443–463 (2017)CrossRefGoogle Scholar
  6. 6.
    Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes Twitter users: real-time event detection by social sensors. In: WWW, pp. 851–860 (2010)Google Scholar
  7. 7.
    Wilson, T., Medine, P.: The Art of Rhetoric (1560): G - Reference Information and Interdisciplinary Subjects Series. Pennsylvania State University Press (1999)Google Scholar
  8. 8.
    Chakma, K., Das, A., Debbarma, S.: Deep semantic role labeling for tweets using 5W1H: Who, What, When, Where, Why and How. Computación y Sistemas 23(3), 751–763 (2019).  https://doi.org/10.13053/CyS-23-3-3253CrossRefGoogle Scholar
  9. 9.
    Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. In: Proceedings of the ICLR Conference, San Diego, USA, pp. 1–15 (2015)Google Scholar
  10. 10.
    Gildea, D., Jurafsky, D.: Automatic labeling of semantic roles. Assoc. Comput. Linguist. 28(3), 245–288 (2002)CrossRefGoogle Scholar
  11. 11.
    Palmer, M., Gildea, D., Kingsbury, P.: The proposition bank: a corpus annotated with semantic roles. Comput. Linguist. J. 31(1), 71–105 (2005)CrossRefGoogle Scholar
  12. 12.
    Baker, C.F., Fillmore, C.J., Lowe, J.B.: The Berkeley framenet project. In: 1998 Proceedings of the 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, vol. 1, pp. 86–90 (1998)Google Scholar
  13. 13.
    Schuler, K.K., Palmer, M.S.: VerbNet: abroad-coverage, comprehensive verb lexicon. Ph.D. thesis. University of Pennsylvania, Philadelphia, PA, USA (2005)Google Scholar
  14. 14.
    Zhao, Z., Sun, J., Mao, Z., Feng, S., Bao, Y.: Determining the topic hashtags for Chinese microblogs based on 5W model. In: Wang, Y., Yu, G., Zhang, Y., Han, Z., Wang, G. (eds.) BigCom 2016. LNCS, vol. 9784, pp. 55–67. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-42553-5_5CrossRefGoogle Scholar
  15. 15.
    Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, vol. 26 (2013)Google Scholar
  16. 16.
    Pennington, J., Socher, R., Manning, C.D.: GloVe: global vectors for word representation. In: Empirical Methods in Natural Language Processing, pp. 1532–1543 (2014)Google Scholar
  17. 17.
    Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)
  18. 18.
    Jin, X., Han, J.: K-means clustering. In: Sammut, C., Webb, G.I. (eds.) Encyclopedia of Machine Learning. Springer, Boston (2011).  https://doi.org/10.1007/978-0-387-30164-8_425CrossRefGoogle Scholar
  19. 19.
    Jin, X., Han, J.: TF-IDF. In: Sammut, C., Webb, G.I. (eds.) Encyclopedia of Machine Learning. Springer, Boston (2011).  https://doi.org/10.1007/978-0-387-30164-8_832CrossRefGoogle Scholar
  20. 20.
    Vaswani, A., et al.: Attention is all you need. CoRR, (abs/1706.03762) (2017)Google Scholar
  21. 21.
    Xiao, H.: BERT-as-service (2018). https://github.com/hanxiao/bert-as-service
  22. 22.
    Dudoit, S., Fridlyand, J.: A prediction-based resampling method for estimating the number of clusters in a dataset. Genome Biol. 3(7), 1–21 (2002).  https://doi.org/10.1186/gb-2002-3-7-research0036CrossRefGoogle Scholar
  23. 23.
    Thalamuthu, A., Mukhopadhyay, I., Zheng, X., Tseng, G.C.: Evaluation and comparison of gene clustering methods in microarray analysis. Bioinformatics 22(19), 2405–2412 (2006)CrossRefGoogle Scholar
  24. 24.
    Reimers, N., Gurevych, I.: Sentence-BERT: sentence embeddings using siamese BERT-networks. In: Proceedings of Empirical Methods in Natural Language Processing. Association for Computational Linguistics (2019). http://arxiv.org/abs/1908.10084
  25. 25.
    Maaten, L., Hinton, G.: Visualizing data using t-SNE (2008)Google Scholar
  26. 26.
    Atefeh, F., Khreich, W.: A survey of techniques for event detection in Twitter. Comput. Intell. 31, 132–164 (2015)MathSciNetCrossRefGoogle Scholar
  27. 27.
    Li, C., Sun, A., Datta, A.: Twevent: segment-based event detection from tweets. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management, CIKM 2012, New York, NY, USA, pp. 155–164 (2012)Google Scholar
  28. 28.
    Morabia, K., Murthy, B., Lalita, N., Malapati, A., Samant, S.: SEDTWik: segmentation-based event detection from tweets using Wikipedia. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop, pp. 77–85 (2019)Google Scholar
  29. 29.
    Dabiri, S., Heaslip, K.: Developing a Twitter-based traffic event detection model using deep learning architectures. Expert Syst. Appl. 118, 425–439 (2019)CrossRefGoogle Scholar
  30. 30.
    Sherstinsky, A.: Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. CoRR, (abs/1808.03314) (2018)Google Scholar
  31. 31.
    Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights Imaging 9(4), 611–629 (2018).  https://doi.org/10.1007/s13244-018-0639-9CrossRefGoogle Scholar
  32. 32.
    Hasan, M., Orgun, M.A., Schwitter, R.: Real-time event detection from the Twitter data stream using the TwitterNews+ Framework. Inf. Process. Manag. 56(3), 1146–1165 (2019)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.National Institute of Technology AgartalaAgartalaIndia
  2. 2.Wipro AI LabBangaloreIndia

Personalised recommendations