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JOURNALS // Zapiski Nauchnykh Seminarov POMI

Zap. Nauchn. Sem. POMI, 2021, Volume 499, Pages 222–235 (Mi znsl7050)

Named entity recognition in Russian using multi-task LSTM-CRF
D. Mazitov, I. Alimova, E. Tutubalina

References

1. M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, et al., “Tensorflow: a system for large-scale machine learning”, OSDI, 16 (2016), 265–283
2. C. Adak, B. B. Chaudhuri, M. Blumenstein, “Named entity recognition from unstructured handwritten document images”, 12th IAPR Workshop on Document Analysis Systems (DAS), 2016, 375–380
3. L. T. Anh, M. Y. Arkhipov, M. S. Burtsev, Application of a hybrid bi-LSTM-CRF model to the task of Russian named entity recognition, Communications in Computer and Information Science book series – CCIS, 789, 2017
4. L.T. Anh, M. Y. Arkhipov, M. S. Burtsev, Application of a hybrid bi-LSTM-CRF model to the task of Russian named entity recognition, 2017, arXiv: 1709.09686
5. A. Y. Antonova, A. N. Soloviev, “Conditional random field models for the processing of Russian”, Communications of the ACM, 56:6 (2013)  zmath
6. M. Y. Arkhipov, M. S. Burtsev, L. T. Anh, “Application of a hybrid bi-LSTM-CRF model to the task of Russian named entity recognition”, Conference on Artificial Intelligence and Natural Language, Springer, Cham, 2017  zmath
7. M. M. Brykina, A. V. Faynveyts, S. Yu. Toldova, “Dictionary-based ambiguity resolution in Russian named entities recognition”, International Workshop on Computational Linguistics and its Applications, v. 1, ed. A. Narin'yani, 2013  zmath
8. R. Chalapathy, E. Z. Borzeshi, M. Piccardi, Bidirectional LSTM-CRF for clinical concept extraction, 2016, arXiv: 1611.08373
9. J.P.C. Chiu, E. Nichols, “Named entity recognition with bidirectional LSTM-cnns”, Transactions of the Association for Computational Linguistics, 4 (2016), 357–370  crossref
10. L.G. Craidlin, “Program of allocation of Russian individualized nominal groups taglite”, Computational linguistics and intellectual technologies Dialog, 2005
11. D. Kingma, J. Ba, “Adam: A method for stochastic optimization”, 3rd International Conference for Learning Representations (San Diego, 2014)
12. C. Dong, J. Zhang, C. Zong, M. Hattori, H. Di, “Character-based LSTM-CRF with radical-level features for chinese named entity recognition”, Natural Language Understanding and Intelligent Applications, Springer, 2016, 239–250  crossref
13. R. Gareev, M. Tkachenko, V. Solovyev, A. Simanovsky, V. Ivanov, “Introducing baselines for Russian named entity recognition”, Computational Linguistics and Intelligent Text Processing, 2013
14. A. Graves, S. Fernández, J. Schmidhuber, “Bidirectional LSTM networks for improved phoneme classification and recognition”, Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005, 2005
15. K. Greff, R. K. Srivastava, J. Koutnik, B. R. Steunebrink, J. Schmidhuber, “LSTM: A search space odyssey”, IEEE Trans Neural Netw Learn Syst., 2016  crossref  zmath  scopus
16. Z. Huang, W. Xu, K. Yu, Bidirectional LSTM-CRF models for sequence tagging, 2015, arXiv: 1508.01991
17. Kaggle, Predict Russian universal dependencies POS tags, 2017
18. G. Konoplich, E. Putin, A. Filchenkov, R. Rybka, “Named entity recognition in Russian with word representation learned by a bidirectional language model”, AINL, 2018
19. G. Konoplich, E. Putin, A. Filchenkov, R. Rybka, “Named entity recognition in Russian with word representation learned by a bidirectional language model”, Conference on Artificial Intelligence and Natural Language, Springer, 2018, 48–58
20. J. Lafferty, A. McCallum, F. Pereira, “Conditional random fields: Probabilistic models for segmenting and labeling sequence data”, Proc. 18th International Conference on Machine Learning, 2001
21. G. Lample, M. Ballesteros, S. Subramanian, K. Kawakami, C. Dyer, “Neural architectures for named entity recognition”, Proc. 2016 NAACL, 2016, 260–270
22. X. Ma, E. Hovy, End-to-end sequence labeling via bi-directional LSTM-CNNs-CRF, 2016, arXiv: 1603.01354
23. V. Malykh, A. Ozerin, “Reproducing Russian ner baseline quality without additional data”, CDUD@ CLA, 2016, 54–59
24. S. Misawa, M. Taniguchi, Y. Miura, T. Ohkuma, “Character-based bidirectional LSTM-CRF with words and characters for japanese named entity recognition”, Proc. 1st Workshop on Subword and Character Level Models in NLP, 2017, 97–102  crossref
25. V. Mozharova, N. Loukachevitch, “Two-stage approach in Russian named entity recognition”, Proc. 2016 International FRUCT Conference on Intelligence, Social Media and Web, ISMW FRUCT, IEEE, 2016, 1–6
26. A. V. Podobryaev, “Searching for person memories in news texts with the use of a model of conditional random fields”, RCDL 2013
27. B. Popov, A. Kiryakov, D. Ognyanoff, D. Manov, A. Kirilov, “Kim — a semantic platform for information extraction and retrieval”, Journal of Natural Language Engineering, 10 (2004)  crossref  zmath
28. R. M. Zavala, P. Martinez, I. Segura-Bedmar, “A hybrid bi-LSTM-CRF model for knowledge recognition from ehealth documents”, TASS 2018: Workshop on Semantic Analysis at SEPLN, 2018, 65–70
29. R. Ivanitskiy, A. Shipilo, L. Kovriguina, “Russian named entities recognition and classification using distributed word and phrase representations”, SIMBig, 2016
30. A. V. Rubaylo, M. Y. Kosenko, Software utilities for natural language information retrievial, Almanac of modern science and education, 12, 2016
31. E. Sheng, S. Miller, J.S. Ambite, P. Natarajan, “A neural named entity recognition approach to biological entity identification”, Proc. BioCreative VI Workshop, 2017, 24–27
32. A. S. Starostin, V. V. Bocharov, S. V. Alexeeva, A. Bodrova, A. S. Chuchunkov, S. S. Dzhumaev, M. A. Nikolaeva, “Evaluation of named entity recognition and fact extraction systems for Russian”, Annual International Conference Dialogue, 2016
33. A.A. Sysoev, I.A. Andrianov, “Named entity recognition in Russian: the power of wiki-based approach”, Proc. International Conference Dialogue, 2016, 746–755
34. I. V. Trofimov, “Person name recognition in news articles based on the persons-1000/1111-f collections”, 16th All-Russian Scientific Conference Digital Libraries: Advanced Methods and Technologies, Digital Collections, RCDL, 2014, 217–221
35. E. Tutubalina, S. Nikolenko, “Combination of deep recurrent neural networks and conditional random fields for extracting adverse drug reactions from user reviews”, Journal of Healthcare Engineering, 2017 (2017), 9451342  crossref
36. N. A. Vlasova, E. A. Suleymanova, I. V. Trofimov, “Report on Russian corpus for personal name retrieval”, Proceedings of computational and cognitive linguistics TEL, 2014
37. Q. Wei, T. Chen, R. Xu, Y. He, L. Gui, Disease named entity recognition by combining conditional random fields and bidirectional recurrent neural networks, Database, 2016, 2016  crossref


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