Regardless of numerous efforts at name tagging for Uyghur, there is limited understanding on the performance ceiling. In this paper, we take a close look at the successful cases and perform careful analysis on the remaining errors of a state-of-the-art Uyghur name tagger, systematically categorize challenges, and propose possible solutions. We conclude that simply adopting a machine learning model which is proven successful for high-resource languages along with language-independent superficial features is unlikely to be effective for Uyghur, or low-resource languages in general. Further advancement requires exploiting rich language-specific knowledge and non-traditional linguistic resources, and novel methods to encode them into machine learning frameworks.
@InProceedings{ABUDUKELIMU18.12, author = {Halidanmu Abudukelimu and Adudoukelimu Abulizi and Boliang Zhang and Xiaoman Pan and Di Lu and Heng Ji and Yang Liu}, title = "{Error Analysis of Uyghur Name Tagging: Language-specific Techniques and Remaining Challenges}", 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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