We propose a pipeline through which to derive clusters of dialects, given a mixed corpus composed of di erent dialects, when their standard counterpart is su ciently resourced. The test case is Japanese, where the written standard language is su ciently equipped with adequate resources. Our method starts by detecting non-standard contents rst, and then clusters what is deemed dialectal. We report the results on the clustering of mixed Twitter corpus into four dialects (Kansai, Tohoku, Chugoku and Kyushu).
@InProceedings{SATO18.142, author = {Yo Sato and Kevin Heffernan}, title = "{Creating dialect sub-corpora by clustering: a case in Japanese for an adaptive method}", 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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