This paper describes how a tokenizer can be trained from any dataset in the Universal Dependencies 2.1 corpus. A software tool, which relies on Elephant to perform the training, is also made available. Beyond providing the community with a large choice of language-specific tokenizers, we argue in this paper that: (1) tokenization should be considered as a supervised task; (2) language scalability requires a streamlined software engineering process across languages.
@InProceedings{MOREAU18.1072, author = {Erwan Moreau and Carl Vogel}, title = "{Multilingual Word Segmentation: Training Many Language-Specific Tokenizers Smoothly Thanks to the Universal Dependencies Corpus}", booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {May 7-12, 2018}, address = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, isbn = {979-10-95546-00-9}, language = {english} }
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