Abstract
Information extraction in disaster domain is a critical task for effective disaster management. A high quality event detection system is the very first step towards this. Since disaster annotated data-sets are not available in Indian languages, we first create and annotate a dataset in three different languages, namely Hindi, Bengali and English. The data was crawled from the different news websites and annotated with expert annotators using a proper annotation guidelines. The events in the dataset belong to 35 different disaster classes. We then build a deep ensemble architecture based on Convolution Neural Network (CNN) and Bi-directional Long Short Term Memory (Bi-LSTM) network as the base learning models. This model is used to identify event words and phrases along with its class from the input sentence. Since our data is sparse, the model yields a very low F1-score in all the three languages. To mitigate the data sparsity problem we make use of multi-lingual word embedding so that joint training of all the languages could be done. To accommodate joint training we modify our model to contain language-specific layers so that the syntactic differences between the languages can be taken care of by these layers. By using multi-lingual embedding and training the whole dataset on our proposed model, the performance of event detection in each language improves significantly. We also report further analysis of language-wise and class-wise improvements of each language and event classes.
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The confusion matrices for each language dataset were computed on 80:20 train-test split.
References
Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information. Trans. Assoc. Comput. Linguist. 5, 135–146 (2017)
Cao, K., Li, X., Fan, M., Grishman, R.: Improving event detection with active learning. In: Proceedings of the International Conference Recent Advances in Natural Language Processing, pp. 72–77 (2015)
Cao, K., Li, X., Grishman, R.: Improving event detection with dependency regularization. In: Proceedings of the International Conference Recent Advances in Natural Language Processing, pp. 78–83 (2015)
Chen, Y., Xu, L., Liu, K., Zeng, D., Zhao, J.: Event extraction via dynamic multi-pooling convolutional neural networks. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), vol. 1, pp. 167–176 (2015)
Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)
Feng, X., Qin, B., Liu, T.: A language-independent neural network for event detection. Sci. China Inf. Sci. 61(9), 1–12 (2018). https://doi.org/10.1007/s11432-017-9359-x
Ferguson, J., Lockard, C., Weld, D., Hajishirzi, H.: Semi-supervised event extraction with paraphrase clusters. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers), vol. 2, pp. 359–364 (2018)
Grishman, R., Westbrook, D., Meyers, A.: Nyu’s English ACE 2005 system description. ACE 5 (2005)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Hong, Y., Zhang, J., Ma, B., Yao, J., Zhou, G., Zhu, Q.: Using cross-entity inference to improve event extraction. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, vol. 1, pp. 1127–1136. Association for Computational Linguistics (2011)
Ji, H., Grishman, R.: Refining event extraction through cross-document inference. In: Proceedings of ACL 2008: HLT, pp. 254–262 (2008)
Kim, Y.: Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882 (2014)
Li, Q., Ji, H., Huang, L.: Joint event extraction via structured prediction with global features. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), vol. 1, pp. 73–82 (2013)
Li, X., Nguyen, T.H., Cao, K., Grishman, R.: Improving event detection with abstract meaning representation. In: Proceedings of the First Workshop on Computing News Storylines, pp. 11–15 (2015)
Liao, S., Grishman, R.: Using document level cross-event inference to improve event extraction. In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pp. 789–797. Association for Computational Linguistics (2010)
Liao, S., Grishman, R.: Acquiring topic features to improve event extraction: in pre-selected and balanced collections. In: Proceedings of the International Conference Recent Advances in Natural Language Processing 2011, pp. 9–16 (2011)
Lin, Y., Yang, S., Stoyanov, V., Ji, H.: A multi-lingual multi-task architecture for low-resource sequence labeling. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), vol. 1, pp. 799–809 (2018)
Liu, J., Chen, Y., Liu, K., Zhao, J.: Event detection via gated multilingual attention mechanism. Statistics 1000, 1250 (2018)
Liu, S., Chen, Y., He, S., Liu, K., Zhao, J.: Leveraging framenet to improve automatic event detection. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), vol. 1, pp. 2134–2143 (2016)
Liu, S., Chen, Y., Liu, K., Zhao, J.: Exploiting argument information to improve event detection via supervised attention mechanisms. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), vol. 1, pp. 1789–1798 (2017)
Nguyen, T.H., Cho, K., Grishman, R.: Joint event extraction via recurrent neural networks. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 300–309 (2016)
Nguyen, T.H., Grishman, R.: Event detection and domain adaptation with convolutional neural networks. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), vol. 2, pp. 365–371 (2015)
Nguyen, T.H., Grishman, R.: Modeling skip-grams for event detection with convolutional neural networks. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 886–891 (2016)
Nguyen, T.H., Grishman, R.: Graph convolutional networks with argument-aware pooling for event detection (2018)
Orr, J.W., Tadepalli, P., Fern, X.: Event detection with neural networks: a rigorous empirical evaluation. arXiv preprint arXiv:1808.08504 (2018)
Ramshaw, L.A., Marcus, M.P.: Text chunking using transformation-based learning. In: Armstrong, S., Church, K., Isabelle, P., Manzi, S., Tzoukermann, E., Yarowsky, D. (eds.) Natural Language Processing Using Very Large Corpora, pp. 157–176. Springer, Dordrecht (1999). https://doi.org/10.1007/978-94-017-2390-9_10
Sha, L., Qian, F., Chang, B., Sui, Z.: Jointly extracting event triggers and arguments by dependency-bridge RNN and tensor-based argument interaction. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)
Smith, S.L., Turban, D.H., Hamblin, S., Hammerla, N.Y.: Offline bilingual word vectors, orthogonal transformations and the inverted softmax. arXiv preprint arXiv:1702.03859 (2017)
Tanev, H., Piskorski, J., Atkinson, M.: Real-time news event extraction for global crisis monitoring. In: Kapetanios, E., Sugumaran, V., Spiliopoulou, M. (eds.) NLDB 2008. LNCS, vol. 5039, pp. 207–218. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-69858-6_21
Yang, B., Mitchell, T.: Joint extraction of events and entities within a document context. arXiv preprint arXiv:1609.03632 (2016)
Acknowledgement
The research reported in this paper is an outcome of the project titled “A Platform for Cross-lingual and Multi-lingual Event Monitoring in Indian Languages”, supported by IMPRINT-1, MHRD, Govt. of India, and MeiTY, Govt. of India. The Bengali and English language datasets used in the experiments, were created by the project partners at Indian Institute of Technology Kharagpur, and Anna University - K. B. Chandrashekar Research Centre respectively.
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Ahmad, Z., Varshney, D., Ekbal, A., Bhattacharyya, P. (2023). Multi-lingual Event Identification in Disaster Domain. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2019. Lecture Notes in Computer Science, vol 13451. Springer, Cham. https://doi.org/10.1007/978-3-031-24337-0_10
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