{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:23:54Z","timestamp":1750220634769,"version":"3.41.0"},"reference-count":31,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2021,3,30]],"date-time":"2021-03-30T00:00:00Z","timestamp":1617062400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61671064 and 61732005"],"award-info":[{"award-number":["61671064 and 61732005"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"National Key RD Program of China","award":["2018YFC0831704"],"award-info":[{"award-number":["2018YFC0831704"]}]}],"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>Dependency parsing is an important task for Natural Language Processing (NLP). However, a mature parser requires a large treebank for training, which is still extremely costly to create. Tibetan is a kind of extremely low-resource language for NLP, there is no available Tibetan dependency treebank, which is currently obtained by manual annotation. Furthermore, there are few related kinds of research on the construction of treebank. We propose a novel method of multi-level chunk-based syntactic parsing to complete constituent-to-dependency treebank conversion for Tibetan under scarce conditions. Our method mines more dependencies of Tibetan sentences, builds a high-quality Tibetan dependency tree corpus, and makes fuller use of the inherent laws of the language itself. We train the dependency parsing models on the dependency treebank obtained by the preliminary transformation. The model achieves 86.5% accuracy, 96% LAS, and 97.85% UAS, which exceeds the optimal results of existing conversion methods. The experimental results show that our method has the potential to use a low-resource setting, which means we not only solve the problem of scarce Tibetan dependency treebank but also avoid needless manual annotation. The method embodies the regularity of strong knowledge-guided linguistic analysis methods, which is of great significance to promote the research of Tibetan information processing.<\/jats:p>","DOI":"10.1145\/3424247","type":"journal-article","created":{"date-parts":[[2021,3,30]],"date-time":"2021-03-30T18:22:42Z","timestamp":1617128562000},"page":"1-12","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi-level Chunk-based Constituent-to-Dependency Treebank Transformation for Tibetan Dependency Parsing"],"prefix":"10.1145","volume":"20","author":[{"given":"Shumin","family":"Shi","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dan","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xing","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Computer Technology, Beijing Institute of Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Congjun","family":"Long","sequence":"additional","affiliation":[{"name":"Institute of Ethnology and Anthropology, Chinese Academy of Social Science, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Heyan","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,3,30]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.3115\/1220575.1220666"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.5555\/1620754.1620790"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/1076034.1076103"},{"key":"e_1_2_1_4_1","first-page":"166","article-title":"Semi-automatic building tibetan treebank based on word-pair dependency classification[J]","volume":"27","author":"Cairang Huaque","year":"2013","unstructured":"Huaque Cairang , Jiang Wenbin , Zhao Haixing , and Liu Qun . 2013 . Semi-automatic building tibetan treebank based on word-pair dependency classification[J] . Journal of Chinese Information Processing 27 , 5 (2013), 166 -- 172 . Huaque Cairang, Jiang Wenbin, Zhao Haixing, and Liu Qun. 2013. Semi-automatic building tibetan treebank based on word-pair dependency classification[J]. Journal of Chinese Information Processing 27, 5 (2013), 166--172.","journal-title":"Journal of Chinese Information Processing"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1082"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1162\/089120103322753356"},{"key":"e_1_2_1_7_1","first-page":"59","article-title":"Longest noun phrases detection in tibetan[J]","volume":"33","author":"Congjun Long","year":"2019","unstructured":"Long Congjun , Liu Huidan , and Zhou Maoke . 2019 . Longest noun phrases detection in tibetan[J] . J. Chin. Inf. Process. 33 , 2 (2019), 59 \u2013 66 . Long Congjun, Liu Huidan, and Zhou Maoke. 2019. Longest noun phrases detection in tibetan[J]. J. Chin. Inf. Process. 33, 2 (2019), 59\u201366.","journal-title":"J. Chin. Inf. Process."},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/1076034.1076103"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1162\/089120103322753356"},{"key":"e_1_2_1_10_1","volume-title":"Manning","author":"Dozat Timothy","year":"2016","unstructured":"Timothy Dozat and Christopher D . Manning . 2016 . Deep biaffine attention for neural dependency parsing. arXiv:1611.01734. Retrieved from https:\/\/arxiv.org\/abs\/1611.01734 Timothy Dozat and Christopher D. Manning. 2016. Deep biaffine attention for neural dependency parsing. arXiv:1611.01734. Retrieved from https:\/\/arxiv.org\/abs\/1611.01734"},{"key":"e_1_2_1_11_1","volume-title":"Manning","author":"Dozat Timothy","year":"2018","unstructured":"Timothy Dozat and Christopher D . Manning . 2018 . Simpler but more accurate semantic dependency parsing. arXiv:1807.01396. Retrieved from https:\/\/arxiv.org\/abs\/1807.01396. Timothy Dozat and Christopher D. Manning. 2018. Simpler but more accurate semantic dependency parsing. arXiv:1807.01396. Retrieved from https:\/\/arxiv.org\/abs\/1807.01396."},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D15-1040"},{"key":"e_1_2_1_13_1","unstructured":"Carlos G\u00f3mez-Rodr\u00edguez Tianze Shi and Lillian Lee. 2018. Global transition-based non-projective dependency parsing. arXiv:1807.01745. Retrieved from https:\/\/arxiv.org\/abs\/1807.01745.  Carlos G\u00f3mez-Rodr\u00edguez Tianze Shi and Lillian Lee. 2018. Global transition-based non-projective dependency parsing. arXiv:1807.01745. Retrieved from https:\/\/arxiv.org\/abs\/1807.01745."},{"key":"e_1_2_1_14_1","first-page":"76","article-title":"Research center of tibetan information technology, tibet university; Northwest university for nationalities; Theory and method of tibetan dependency treebank construction[J]","volume":"30","author":"Gyal Tashi","year":"2015","unstructured":"Tashi Gyal and Duo La . 2015 . Research center of tibetan information technology, tibet university; Northwest university for nationalities; Theory and method of tibetan dependency treebank construction[J] . Tibet. Univ. 30 , 2 (2015), 76 -- 83 . Tashi Gyal and Duo La. 2015. Research center of tibetan information technology, tibet university; Northwest university for nationalities; Theory and method of tibetan dependency treebank construction[J]. Tibet. Univ. 30, 2 (2015), 76--83.","journal-title":"Tibet. Univ."},{"key":"e_1_2_1_15_1","first-page":"300","article-title":"Tibetan text dependency syntactic analysis based on discriminant[J]","volume":"39","author":"Zhao Haixing Hua","year":"2013","unstructured":"Hua quecairang and Zhao Haixing . 2013 . Tibetan text dependency syntactic analysis based on discriminant[J] . Computer Engineering 39 , 4 (2013), 300 -- 304 . Hua quecairang and Zhao Haixing. 2013. Tibetan text dependency syntactic analysis based on discriminant[J]. Computer Engineering 39, 4 (2013), 300--304.","journal-title":"Computer Engineering"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1237"},{"key":"e_1_2_1_17_1","unstructured":"D. Klein and C. D. Manning. 2002. Fast exact inference with a factored model for natural language Parsing. NIPS. 3--10.  D. Klein and C. D. Manning. 2002. Fast exact inference with a factored model for natural language Parsing. NIPS. 3--10."},{"key":"e_1_2_1_18_1","unstructured":"David M. Magerman. 1994. Natural language parsing as statistical pattern recognition. arXiv:cmp-lg\/9405009. Retrieved from http:\/\/arxiv.org\/abs\/cmp-lg\/9405009.  David M. Magerman. 1994. Natural language parsing as statistical pattern recognition. arXiv:cmp-lg\/9405009. Retrieved from http:\/\/arxiv.org\/abs\/cmp-lg\/9405009."},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.3115\/1220575.1220641"},{"key":"e_1_2_1_20_1","doi-asserted-by":"crossref","unstructured":"Joakim Nivre. 2015. Towards a universal grammar for natural language Processing. CICLing 1. 3--16.  Joakim Nivre. 2015. Towards a universal grammar for natural language Processing. CICLing 1. 3--16.","DOI":"10.1007\/978-3-319-18111-0_1"},{"key":"e_1_2_1_21_1","volume-title":"Proceedings of the International Conference on Language Resources and Evaluation (LREC\u201906)","volume":"6","author":"Nivre Joakim","year":"2006","unstructured":"Joakim Nivre , Johan Hall , and Jens Nilsson . 2006 . Maltparser: A data-driven parser-generator for dependency parsing . In Proceedings of the International Conference on Language Resources and Evaluation (LREC\u201906) , Vol. 6 . 2216\u20132219. Joakim Nivre, Johan Hall, and Jens Nilsson. 2006. Maltparser: A data-driven parser-generator for dependency parsing. In Proceedings of the International Conference on Language Resources and Evaluation (LREC\u201906), Vol. 6. 2216\u20132219."},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/P15-1031"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.3115\/1220575.1220641"},{"key":"e_1_2_1_24_1","doi-asserted-by":"crossref","unstructured":"Tianze Shi Liang Huang and Lillian Lee. 2017a. Fast(er) exact decoding and global training for transition-based dependency parsing via a minimal feature set. arXiv:1708.09403. Retrieved from https:\/\/arxiv.org\/abs\/1708.09403.  Tianze Shi Liang Huang and Lillian Lee. 2017a. Fast(er) exact decoding and global training for transition-based dependency parsing via a minimal feature set. arXiv:1708.09403. Retrieved from https:\/\/arxiv.org\/abs\/1708.09403.","DOI":"10.18653\/v1\/D17-1002"},{"key":"e_1_2_1_25_1","first-page":"1642","volume-title":"Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics","author":"Socher Richard","year":"2013","unstructured":"Richard Socher , Alex Perelygin , Jean Wu , Jason Chuang , Christopher D. Manning , Andrew Ng , and Christopher Potts . 2013 . Recursive deep models for semantic compositionality over a sentiment treebank . In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics , pp. 1631\u2013 1642 . Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D. Manning, Andrew Ng, and Christopher Potts. 2013. Recursive deep models for semantic compositionality over a sentiment treebank. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, pp. 1631\u20131642."},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.5555\/2968618.2968619"},{"key":"e_1_2_1_27_1","unstructured":"Xinyu Wang Jingxian Huang and Kewei Tu. 2019. Second-order semantic dependency parsing with end-to-end neural networks. arXiv:1906.07880. Retrieved from https:\/\/arxiv.org\/abs\/1906.07880.  Xinyu Wang Jingxian Huang and Kewei Tu. 2019. Second-order semantic dependency parsing with end-to-end neural networks. arXiv:1906.07880. Retrieved from https:\/\/arxiv.org\/abs\/1906.07880."},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P16-1218"},{"key":"e_1_2_1_29_1","unstructured":"David Weiss Chris Alberti Michael Collins and Slav Petrov. 2015. Structured training for neural network transition-based parsing. arXiv:1506.06158. Retrieved from https:\/\/arxiv.org\/abs\/1506.06158.  David Weiss Chris Alberti Michael Collins and Slav Petrov. 2015. Structured training for neural network transition-based parsing. arXiv:1506.06158. Retrieved from https:\/\/arxiv.org\/abs\/1506.06158."},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.3115\/1620754.1620790"},{"key":"e_1_2_1_31_1","first-page":"206","volume-title":"Proceedings of the 8th International Conference on Parsing Technologies.","author":"Yamada Hiroyasu","year":"2003","unstructured":"Hiroyasu Yamada and Yuji Matsumoto . 2003 . Statistical dependency analysis with support vector machines . In Proceedings of the 8th International Conference on Parsing Technologies. pp. 195\u2013 206 . Hiroyasu Yamada and Yuji Matsumoto. 2003. Statistical dependency analysis with support vector machines. In Proceedings of the 8th International Conference on Parsing Technologies. pp. 195\u2013206."}],"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\/3424247","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3424247","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:01:51Z","timestamp":1750197711000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3424247"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,30]]},"references-count":31,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2021,3,31]]}},"alternative-id":["10.1145\/3424247"],"URL":"https:\/\/doi.org\/10.1145\/3424247","relation":{},"ISSN":["2375-4699","2375-4702"],"issn-type":[{"type":"print","value":"2375-4699"},{"type":"electronic","value":"2375-4702"}],"subject":[],"published":{"date-parts":[[2021,3,30]]},"assertion":[{"value":"2020-01-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-09-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-03-30","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}