Event Registry titles dataset texts with OCR degradations

This is the text of the Event Registry titles: Rupnik, Jan, Andrej Muhic, Gregor Leban, Primoz Skraba, Blaz Fortuna, et Marko Grobelnik. 2016. « News Across Languages - Cross-Lingual Document Similarity and Event Tracking ». <em>Journal of Artificial Intelligence Research</em> 55 (janvier): 283‑316. https://doi.org/10.1613/jair.4780. Miranda, Sebastião, Artūrs Znotiņš, Shay B. Cohen, et Guntis Barzdins. 2018. « Multilingual Clustering of Streaming News ». In <em>2018 Conference on Empirical Methods in Natural Language Processing</em>, 4535‑44. Brussels, Belgium: Association for Computational Linguistics. https://www.aclweb.org/anthology/D18-1483/. Guillaume Bernard. (2022). Event Registry titles only dataset with multiple extracted features (both sparse and dense) (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6630447 Some degradations are applied using the DocCreator [1] tool in order to degrade the text of the tweets and to reproduce some common errors found in OCRised documents [2]. [1]: Journet, Nicholas, Muriel Visani, Boris Mansencal, Kieu Van-Cuong, et Antoine Billy. 2017. « DocCreator: A New Software for Creating Synthetic Ground-Truthed Document Images ». <em>Journal of Imaging</em> 3 (4): 62. https://doi.org/10.3390/jimaging3040062. [2]: Linhares Pontes, Elvys, Ahmed Hamdi, Nicolas Sidere, et Antoine Doucet. 2019. « Impact of OCR Quality on Named Entity Linking ». In <em>Digital Libraries at the Crossroads of Digital Information for the Future</em>, 11853:102‑15. Lecture Notes in Computer Science. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-34058-2_11. The results of the OCR degradations are as follow: FibVid CER/WER Without Character degradation Phantom degradation Bleed Blur All Event Registry Titles CER 2.421 6.940 2.414 2.422 2.874 7.178 Event Registry Titles WER 1.127 19.785 1.124 1.131 2.035 19.894