ML Anthology

Placing (Historical) Facts on a Timeline: A Classification Cum Coref Resolution Approach

Sayantan Adak, Altaf Ahmad, Aditya Basu, Animesh Mukherjee
ECML-PKDD 2022 pp. 335-352
doi:10.1007/978-3-031-26422-1_21 /ecmlpkdd/2022/adak2022ecmlpkdd-placing/

Abstract

A timeline provides one of the most effective ways to visualize the important historical facts that occurred over a period of time, presenting the insights that may not be so apparent from reading the equivalent information in textual form . By leveraging generative adversarial learning for important sentence classification and by assimilating knowledge based tags for improving the performance of event coreference resolution we introduce a two staged system for event timeline generation from multiple (historical) text documents. We demonstrate our results on two manually annotated historical text documents. Our results can be extremely helpful for historians, in advancing research in history and in understanding the socio-political landscape of a country as reflected in the writings of famous personas. The dataset and the code are available at https://github.com/sayantan11995/Event-Timeline-Generation-from-Documents .

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Cite

Text

Adak et al. "Placing (Historical) Facts on a Timeline: A Classification Cum Coref Resolution Approach." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2022. doi:10.1007/978-3-031-26422-1_21

Markdown

[Adak et al. "Placing (Historical) Facts on a Timeline: A Classification Cum Coref Resolution Approach." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2022.](https://mlanthology.org/ecmlpkdd/2022/adak2022ecmlpkdd-placing/) doi:10.1007/978-3-031-26422-1_21

BibTeX

@inproceedings{adak2022ecmlpkdd-placing,
  title     = {{Placing (Historical) Facts on a Timeline: A Classification Cum Coref Resolution Approach}},
  author    = {Adak, Sayantan and Ahmad, Altaf and Basu, Aditya and Mukherjee, Animesh},
  booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
  year      = {2022},
  pages     = {335-352},
  doi       = {10.1007/978-3-031-26422-1_21},
  url       = {https://mlanthology.org/ecmlpkdd/2022/adak2022ecmlpkdd-placing/}
}