{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:23:09Z","timestamp":1750220589454,"version":"3.41.0"},"reference-count":12,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2021,3,31]],"date-time":"2021-03-31T00:00:00Z","timestamp":1617148800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Asian Low-Resour. Lang. Inf. Process."],"published-print":{"date-parts":[[2021,3,31]]},"abstract":"<jats:p>Part-of-speech (POS) tagging is a fundamental task in natural language processing. Korean POS tagging consists of two subtasks: morphological analysis and POS tagging. In recent years, scholars have tended to use the seq2seq model to solve this problem. The full context of a sentence is considered in these seq2seq-based Korean POS tagging methods. However, Korean morphological analysis relies more on local contextual information, and in many cases, there exists one-to-one matching between morpheme surface form and base form. To make better use of these characteristics, we propose a hierarchical seq2seq model. In our model, the low-level Bi-LSTM encodes the syllable sequence, whereas the high-level Bi-LSTM models the context information of the whole sentence, and the decoder generates the morpheme base form syllables as well as the POS tags. To improve the accuracy of the morpheme base form recovery, we introduced the convolution layer and the attention mechanism to our model. The experimental results on the Sejong corpus show that our model outperforms strong baseline systems in both morpheme-level F1-score and eojeol-level accuracy, achieving state-of-the-art performance.<\/jats:p>","DOI":"10.1145\/3421762","type":"journal-article","created":{"date-parts":[[2021,4,23]],"date-time":"2021-04-23T16:55:14Z","timestamp":1619196914000},"page":"1-13","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["A Hierarchical Sequence-to-Sequence Model for Korean POS Tagging"],"prefix":"10.1145","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1835-1613","authenticated-orcid":false,"given":"Guozhe","family":"Jin","sequence":"first","affiliation":[{"name":"Jilin University, Yanji, Jilin Province, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhezhou","family":"Yu","sequence":"additional","affiliation":[{"name":"Jilin University, Yanji, Jilin Province, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,4,23]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2016.7841061"},{"key":"e_1_2_1_2_1","volume-title":"Proceedings of the International Conference on Language Resources and Evaluation (LREC\u201918)","author":"Grave Edouard","year":"2018","unstructured":"Edouard Grave , Piotr Bojanowski , Prakhar Gupta , Armand Joulin , and Tomas Mikolov . 2018 . Learning word vectors for 157 languages . In Proceedings of the International Conference on Language Resources and Evaluation (LREC\u201918) . Edouard Grave, Piotr Bojanowski, Prakhar Gupta, Armand Joulin, and Tomas Mikolov. 2018. Learning word vectors for 157 languages. In Proceedings of the International Conference on Language Resources and Evaluation (LREC\u201918)."},{"key":"e_1_2_1_3_1","unstructured":"Zhiheng Huang Wei Xu and Kai Yu. 2015. Bidirectional LSTM-CRF models for sequence tagging. arXiv:1508.01991  Zhiheng Huang Wei Xu and Kai Yu. 2015. Bidirectional LSTM-CRF models for sequence tagging. arXiv:1508.01991"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3178458"},{"key":"e_1_2_1_5_1","first-page":"826","article-title":"Joint models for Korean word spacing and POS tagging using structural SVM","volume":"40","author":"Lee Changki","year":"2013","unstructured":"Changki Lee , Junseok Kim , Jeonghee Kim , and Hyunki Kim . 2013 . Joint models for Korean word spacing and POS tagging using structural SVM . Journal of KIISE: Software and Applications 40 , 12 (2013), 826 \u2013 832 . Changki Lee, Junseok Kim, Jeonghee Kim, and Hyunki Kim. 2013. Joint models for Korean word spacing and POS tagging using structural SVM. Journal of KIISE: Software and Applications 40, 12 (2013), 826\u2013832.","journal-title":"Journal of KIISE: Software and Applications"},{"key":"e_1_2_1_6_1","volume-title":"Proceedings of the 27th International Conference on Computational Linguistics. 2482\u20132492","author":"Matteson Andrew","year":"2018","unstructured":"Andrew Matteson , Chanhee Lee , Youngbum Kim , and Heui-Seok Lim . 2018 . Rich character-level information for Korean morphological analysis and part-of-speech tagging . In Proceedings of the 27th International Conference on Computational Linguistics. 2482\u20132492 . Andrew Matteson, Chanhee Lee, Youngbum Kim, and Heui-Seok Lim. 2018. Rich character-level information for Korean morphological analysis and part-of-speech tagging. In Proceedings of the 27th International Conference on Computational Linguistics. 2482\u20132492."},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/2700051"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.19066\/cogsci.2011.22.3.005"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1150"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3373608"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.5555\/2969033.2969173"},{"key":"e_1_2_1_12_1","doi-asserted-by":"crossref","unstructured":"Wen Zhang Yang Feng Fandong Meng Di You and Qun Liu. 2019. Bridging the gap between training and inference for neural machine translation. arXiv:1906.02448  Wen Zhang Yang Feng Fandong Meng Di You and Qun Liu. 2019. Bridging the gap between training and inference for neural machine translation. arXiv:1906.02448","DOI":"10.18653\/v1\/P19-1426"}],"container-title":["ACM Transactions on Asian and Low-Resource Language Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3421762","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3421762","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T21:31:43Z","timestamp":1750195903000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3421762"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,31]]},"references-count":12,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2021,3,31]]}},"alternative-id":["10.1145\/3421762"],"URL":"https:\/\/doi.org\/10.1145\/3421762","relation":{},"ISSN":["2375-4699","2375-4702"],"issn-type":[{"type":"print","value":"2375-4699"},{"type":"electronic","value":"2375-4702"}],"subject":[],"published":{"date-parts":[[2021,3,31]]},"assertion":[{"value":"2020-03-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-11-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-04-23","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}