Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/92348
Title: | Character profiling in low-resource language documents | Authors: | Wong, TS Lee, J |
Issue Date: | 2019 | Source: | In G Demartini & P Thomas (Eds.), ADCS 2019 : proceedings of the 24th Australasian Document Computing Symposium : Sydney, Australia, December 5-6, 2019. New York, NY, United States : Association for Computing Machinery, 2019. | Abstract: | This paper focuses on automatic character profiling — connecting “who”, “what” and “when” — in literary documents. This task is especially challenging for low-resource languages, since off-the-shelf tools for named entity recognition, syntactic parsing and other natural language processing tasks are rarely available. We investigate the impact of human annotation on automatic profiling. Based on a Medieval Chinese corpus, experimental results show that even a relatively small amount of word segmentation, part-of-speech and dependency annotation can improve accuracy in named entity recognition and in identifying character-verb associations, but not character-toponym associations. | Keywords: | Dependency parsing Information extraction Low-resource language Medieval Chinese Named entity recognition |
Publisher: | Association for Computing Machinery | ISBN: | 978-1-4503-7766-9 | DOI: | 10.1145/3372124.3372129 | Rights: | © 2019 Association for Computing Machinery. This is the accepted version of the publication Tak-sum Wong and John Lee. 2019. Character Profiling in Low-Resource Language Documents. In Proceedings of the 24th Australasian Document Computing Symposium (ADCS '19). Association for Computing Machinery, New York, NY, USA, Article 5, 1-4. The final published version of record is available at https://dx.doi.org/10.1145/3372124.3372129 |
Appears in Collections: | Conference Paper |
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File | Description | Size | Format | |
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ADCS2019_Buddhist_cameraready.pdf | Pre-Published version | 394 kB | Adobe PDF | View/Open |
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