Abstract
Several high-level tasks in the management of Digital Libraries require the application of Natural Language Processing (NLP) techniques. In turn, most NLP solutions are based on linguistic resources that are costly to produce, and so motivate research for automated ways to build them. In particular, Language Identification is a crucial NLP task, that is preliminary to almost all the others, since different linguistic resources must be used for different languages. This paper investigates process mining and management approaches as a possible solution to the Language Identification problem. Specifically, it casts language identification as a process prediction task, and exploits the WoMan framework to carry it out. Experimental results are encouraging and suggest to further explore this approach.
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Acknowledgments
This work was partially funded by the Italian PON 2007–2013 project PON02_00563_3489339 ‘Puglia@Service’.
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Ferilli, S., Esposito, F., Redavid, D., Angelastro, S. (2017). Language Identification as Process Prediction Using WoMan. In: Grana, C., Baraldi, L. (eds) Digital Libraries and Archives. IRCDL 2017. Communications in Computer and Information Science, vol 733. Springer, Cham. https://doi.org/10.1007/978-3-319-68130-6_13
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DOI: https://doi.org/10.1007/978-3-319-68130-6_13
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