{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:34:17Z","timestamp":1750221257355,"version":"3.41.0"},"reference-count":44,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2018,12,14]],"date-time":"2018-12-14T00:00:00Z","timestamp":1544745600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Institute for Information 8 communications Technology Promotion"},{"name":"Korea government","award":["R7119-16-1001"],"award-info":[{"award-number":["R7119-16-1001"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Asian Low-Resour. Lang. Inf. Process."],"published-print":{"date-parts":[[2019,6,30]]},"abstract":"<jats:p>\n            Recently, neural approaches for transition-based dependency parsing have become one of the state-of-the art methods for performing dependency parsing tasks in many languages. In neural transition-based parsing, a\n            <jats:italic>parser state representation<\/jats:italic>\n            is first computed from the configuration of a stack and a buffer, which is then fed into a feed-forward neural network model that predicts the next transition action. Given that words are basic elements of a stack and buffer, a parser state representation is considerably affected by how a\n            <jats:italic>word representation<\/jats:italic>\n            is defined. In particular, word representation issues become more critical in morphologically rich languages such as Korean, as the set of potential words is not bound but introduce the second-order vocabulary complexity, called the\n            <jats:italic>phrase vocabulary complexity<\/jats:italic>\n            due to the agglutinative characteristics of the language. In this article, we propose a\n            <jats:italic>hybrid<\/jats:italic>\n            word representation that combines two compositional word representations, each of which is derived from representations of\n            <jats:italic>syllables<\/jats:italic>\n            and\n            <jats:italic>morphemes<\/jats:italic>\n            , respectively. Our underlying assumption for this hybrid word representation is that, because both syllables and morphemes are two common ways of decomposing Korean words, it is expected that their effects in inducing word representation are complementary to one another. Experimental results carried on Sejong and SPMRL 2014 datasets show that our proposed hybrid word representation leads to the state-of-the-art performance.\n          <\/jats:p>","DOI":"10.1145\/3241745","type":"journal-article","created":{"date-parts":[[2018,12,14]],"date-time":"2018-12-14T13:19:17Z","timestamp":1544793557000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Transition-Based Korean Dependency Parsing Using Hybrid Word Representations of Syllables and Morphemes with LSTMs"],"prefix":"10.1145","volume":"18","author":[{"given":"Seung-hoon","family":"Na","sequence":"first","affiliation":[{"name":"Chonbuk National University, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianri","family":"Li","sequence":"additional","affiliation":[{"name":"Pohang University of Science and Technology (POSTECH), Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jong-hoon","family":"Shin","sequence":"additional","affiliation":[{"name":"Electronics and Telecommunications Research Institute, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kangil","family":"Kim","sequence":"additional","affiliation":[{"name":"Konkuk University, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2018,12,14]]},"reference":[{"doi-asserted-by":"publisher","key":"e_1_2_1_1_1","DOI":"10.18653\/v1\/P16-1231"},{"volume-title":"Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP\u201915)","author":"Ballesteros Miguel","unstructured":"Miguel Ballesteros , Chris Dyer , and Noah A. Smith . 2015. Improved transition-based parsing by modeling characters instead of words with LSTMs . In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP\u201915) . 349--359. Miguel Ballesteros, Chris Dyer, and Noah A. Smith. 2015. Improved transition-based parsing by modeling characters instead of words with LSTMs. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP\u201915). 349--359.","key":"e_1_2_1_2_1"},{"key":"e_1_2_1_3_1","volume-title":"Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP\u201916). 2005","author":"Ballesteros Miguel","year":"2010","unstructured":"Miguel Ballesteros , Yoav Goldberg , Chris Dyer , and Noah A. Smith . 2016. Training with exploration improves a greedy stack LSTM parser . In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP\u201916). 2005 -- 2010 . Miguel Ballesteros, Yoav Goldberg, Chris Dyer, and Noah A. Smith. 2016. Training with exploration improves a greedy stack LSTM parser. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP\u201916). 2005--2010."},{"doi-asserted-by":"publisher","key":"e_1_2_1_4_1","DOI":"10.1561\/2200000006"},{"key":"e_1_2_1_5_1","volume-title":"Proceedings of the 1st Joint Workshop on Statistical Parsing of Morphologically Rich Languages and Syntactic Analysis of Non-Canonical Languages. 97--102","author":"Bj\u00f6rkelund Anders","year":"2014","unstructured":"Anders Bj\u00f6rkelund , \u00d6zlem \u00c7etino\u011flu , Agnieszka Fale\u0144ska , Rich\u00e1rd Farkas , Thomas Mueller , Wolfgang Seeker , and Zsolt Sz\u00e1nt\u00f3 . 2014 . Introducing the IMS-Wroc\u0142aw-Szeged-CIS entry at the SPMRL 2014 shared task: Reranking and morpho-syntax meet unlabeled data . In Proceedings of the 1st Joint Workshop on Statistical Parsing of Morphologically Rich Languages and Syntactic Analysis of Non-Canonical Languages. 97--102 . Anders Bj\u00f6rkelund, \u00d6zlem \u00c7etino\u011flu, Agnieszka Fale\u0144ska, Rich\u00e1rd Farkas, Thomas Mueller, Wolfgang Seeker, and Zsolt Sz\u00e1nt\u00f3. 2014. Introducing the IMS-Wroc\u0142aw-Szeged-CIS entry at the SPMRL 2014 shared task: Reranking and morpho-syntax meet unlabeled data. In Proceedings of the 1st Joint Workshop on Statistical Parsing of Morphologically Rich Languages and Syntactic Analysis of Non-Canonical Languages. 97--102."},{"doi-asserted-by":"publisher","key":"e_1_2_1_6_1","DOI":"10.18653\/v1\/W15-2210"},{"key":"e_1_2_1_7_1","volume-title":"Manning","author":"Chen Danqi","year":"2014","unstructured":"Danqi Chen and Christopher D . Manning . 2014 . A fast and accurate dependency parser using neural networks. In Empirical Methods in Natural Language Processing (EMNLP\u2019 14). 740--750. 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