References¶
- Bojanowski et al. 2017
Bojanowski P., Grave E., Joulin A., & Mikolov T. Enriching word vectors with subword information. Transactions of the Association for Computational Linguistics 5, 135–146 (2017).
- Buchholz & Marsi 2006
Buchholz S. & Marsi E. CoNLL-X shared task on multilingual dependency parsing. In Proceedings of the Tenth Conference on Computational Natural Language Learning (CoNLL-X), 149–164. New York City, June 2006. Association for Computational Linguistics. URL: https://www.aclweb.org/anthology/W06-2920.
- Clark et al. 2019
Clark K., Luong M., Khandelwal U., Manning C., & Le Q. BAM! born-again multi-task networks for natural language understanding. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 5931–5937. Florence, Italy, July 2019. Association for Computational Linguistics. URL: https://www.aclweb.org/anthology/P19-1595, doi:10.18653/v1/P19-1595.
- Collins & Koo 2005
Collins M. & Koo T. Discriminative reranking for natural language parsing. Computational Linguistics 31, 25–70 (2005).
- De 1959
De R. File searching using variable length keys. In Papers Presented at the the March 3-5, 1959, Western Joint Computer Conference, IRE-AIEE-ACM ‘59 (Western), 295–298. New York, NY, USA, 1959. Association for Computing Machinery. URL: https://doi.org/10.1145/1457838.1457895, doi:10.1145/1457838.1457895.
- Devlin et al. 2019
Devlin J., Chang M., Lee K., & Toutanova K. BERT: pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), 4171–4186. Minneapolis, Minnesota, June 2019. Association for Computational Linguistics. URL: https://www.aclweb.org/anthology/N19-1423, doi:10.18653/v1/N19-1423.
- Dozat & Manning 2017
Dozat T. & Manning C. Deep Biaffine Attention for Neural Dependency Parsing. In Proceedings of the 5th International Conference on Learning Representations, ICLR’17. 2017. URL: https://openreview.net/pdf?id=Hk95PK9le.
- Dozat et al. 2017
Dozat T., Qi P., & Manning C. Stanford’s graph-based neural dependency parser at the conll 2017 shared task. In Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, 20–30. 2017.
- He & Choi 2020
He H. & Choi J. Establishing strong baselines for the new decade: sequence tagging, syntactic and semantic parsing with bert. In The Thirty-Third International Flairs Conference. 2020.
- He et al. 2019
He H., Wu L., Yan H., Gao Z., Feng Y., et al. Effective neural solution for multi-criteria word segmentation. In Smart Intelligent Computing and Applications, pages 133–142. Springer, 2019.
- He et al. 2018a
He H., Wu L., Yang X., Yan H., Gao Z., et al. Dual long short-term memory networks for sub-character representation learning. In Information Technology-New Generations, pages 421–426. Springer, 2018a.
- He et al. 2018b
He L., Lee K., Levy O., & Zettlemoyer L. Jointly predicting predicates and arguments in neural semantic role labeling. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 364–369. Melbourne, Australia, July 2018b. Association for Computational Linguistics. URL: https://www.aclweb.org/anthology/P18-2058, doi:10.18653/v1/P18-2058.
- Koehn 2005
Koehn P. Europarl: a parallel corpus for statistical machine translation. In MT summit, volume 5, 79–86. Citeseer, 2005.
- Kondratyuk & Straka 2019
Kondratyuk D. & Straka M. 75 languages, 1 model: parsing universal dependencies universally. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), 2779–2795. Hong Kong, China, 2019. Association for Computational Linguistics. URL: https://www.aclweb.org/anthology/D19-1279.
- Lafferty et al. 2001
Lafferty J., McCallum A., & Pereira F. Conditional random fields: probabilistic models for segmenting and labeling sequence data. Departmental Papers (CIS), (2001).
- Lan et al. 2020
Lan Z., Chen M., Goodman S., Gimpel K., Sharma P., et al. Albert: a lite bert for self-supervised learning of language representations. In International Conference on Learning Representations. 2020. URL: https://openreview.net/forum?id=H1eA7AEtvS.
- Levow 2006
Levow G. The third international Chinese language processing bakeoff: word segmentation and named entity recognition. In Proceedings of the Fifth SIGHAN Workshop on Chinese Language Processing, 108–117. Sydney, Australia, July 2006. Association for Computational Linguistics. URL: https://www.aclweb.org/anthology/W06-0115.
- Pennington et al. 2014
Pennington J., Socher R., & Manning C. GloVe: global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 1532–1543. Doha, Qatar, October 2014. Association for Computational Linguistics. URL: https://www.aclweb.org/anthology/D14-1162, doi:10.3115/v1/D14-1162.
- Pradhan et al. 2012
Pradhan S., Moschitti A., Xue N., Uryupina O., & Zhang Y. CoNLL-2012 shared task: modeling multilingual unrestricted coreference in OntoNotes. In Joint Conference on EMNLP and CoNLL - Shared Task, 1–40. Jeju Island, Korea, July 2012. Association for Computational Linguistics. URL: https://www.aclweb.org/anthology/W12-4501.
- Schweter & Ahmed 2019
Schweter S. & Ahmed S. Deep-EOS: General-Purpose Neural Networks for Sentence Boundary Detection. In Proceedings of the 15th Conference on Natural Language Processing (KONVENS). 2019. accepted.
- Smith & Smith 2007
Smith D. & Smith N. Probabilistic models of nonprojective dependency trees. In Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), 132–140. Prague, Czech Republic, June 2007. Association for Computational Linguistics. URL: https://www.aclweb.org/anthology/D07-1014.
- Tjong & De 2003
Tjong E. & De F. Introduction to the CoNLL-2003 shared task: language-independent named entity recognition. In Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003, 142–147. 2003. URL: https://www.aclweb.org/anthology/W03-0419.
- Wang & Xu 2017
Wang C. & Xu B. Convolutional neural network with word embeddings for Chinese word segmentation. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), 163–172. Taipei, Taiwan, November 2017. Asian Federation of Natural Language Processing. URL: https://www.aclweb.org/anthology/I17-1017.
- Yu et al. 2020
Yu J., Bohnet B., & Poesio M. Named entity recognition as dependency parsing. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 6470–6476. Online, July 2020. Association for Computational Linguistics. URL: https://www.aclweb.org/anthology/2020.acl-main.577, doi:10.18653/v1/2020.acl-main.577.
- Zhang et al. 2020
Zhang Y., Zhou H., & Li Z. Fast and accurate neural crf constituency parsing. In Bessiere C., editor, Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI-20, 4046–4053. International Joint Conferences on Artificial Intelligence Organization, 7 2020. Main track. URL: https://doi.org/10.24963/ijcai.2020/560, doi:10.24963/ijcai.2020/560.
- Zhang & Clark 2008
Zhang Y. & Clark S. A tale of two parsers: Investigating and combining graph-based and transition-based dependency parsing. In Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing, 562–571. Honolulu, Hawaii, October 2008. Association for Computational Linguistics. URL: https://www.aclweb.org/anthology/D08-1059.