Word Segmentation for Akkadian Cuneiform. Homburg, T. & Chiarcos, C. In Calzolari, N., Choukri, K., Declerck, T., Goggi, S., Grobelnik, M., Maegaard, B., Mariani, J., Mazo, H., Moreno, A., Odijk, J., & Piperidis, S., editors, Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016), Paris, France. European Language Resources Association (ELRA). event-place: Portorož, Slovenia
Word Segmentation for Akkadian Cuneiform [pdf]Paper  abstract   bibtex   
We present experiments on word segmentation for Akkadian cuneiform, an ancient writing system and a language used for about 3 millennia in the ancient Near East. To our best knowledge, this is the first study of this kind applied to either the Akkadian language or the cuneiform writing system. As a logosyllabic writing system, cuneiform structurally resembles Eastern Asian writing systems, so, we employ word segmentation algorithms originally developed for Chinese and Japanese. We describe results of rule-based algorithms, dictionary-based algorithms, statistical and machine learning approaches. Our results may indicate possible promising steps in cuneiform word segmentation that can create and improve natural language processing in this area.
@inproceedings{homburg_word_nodate,
	address = {Paris, France},
	title = {Word {Segmentation} for {Akkadian} {Cuneiform}},
	copyright = {All rights reserved},
	isbn = {978-2-9517408-9-1},
	url = {http://www.lrec-conf.org/proceedings/lrec2016/pdf/816_Paper.pdf},
	abstract = {We present experiments on word segmentation for Akkadian cuneiform, an ancient writing system and a language used for about 3 millennia in the ancient Near East. To our best knowledge, this is the first study of this kind applied to either the Akkadian language or the cuneiform writing system. As a logosyllabic writing system, cuneiform structurally resembles Eastern Asian writing systems, so, we employ word segmentation algorithms originally developed for Chinese and Japanese. We describe results of rule-based algorithms, dictionary-based algorithms, statistical and machine learning approaches. Our results may indicate possible promising steps in cuneiform word segmentation that can create and improve natural language processing in this area.},
	language = {english},
	booktitle = {Proceedings of the {Tenth} {International} {Conference} on {Language} {Resources} and {Evaluation} ({LREC} 2016)},
	publisher = {European Language Resources Association (ELRA)},
	author = {Homburg, Timo and Chiarcos, Christian},
	editor = {Calzolari, Nicoletta and Choukri, Khalid and Declerck, Thierry and Goggi, Sara and Grobelnik, Marko and Maegaard, Bente and Mariani, Joseph and Mazo, Hélène and Moreno, Asunción and Odijk, Jan and Piperidis, Stelios},
	note = {event-place: Portorož, Slovenia},
}
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