Elamur Research
☰
home / papers / 2020 / A Unified MRC Framework for Named Entity Recognition

A Unified MRC Framework for Named Entity Recognition

Written for the Research library (claude-opus-5-5) from the paper text. Original — aclanthology.org/2020.acl-main.519.pdf

Summary

Named entity recognition is usually split into two settings. In flat NER, entities never overlap. In nested NER, one mention can sit inside another. Standard sequence labeling gives each token only one tag, so it cannot represent nested structures, and earlier nested-NER systems often relied on pipelines or hand-crafted features. The authors propose one framework for both settings. They recast NER as SQuAD-style machine reading comprehension (MRC): each entity type becomes a natural-language query, and a BERT-based reader marks the start and end positions of matching spans in the text. Overlapping entities of different types are then simply answers to separate questions. The queries are written from dataset annotation guidelines, so they also give the model prior knowledge about each category. The approach achieved new state-of-the-art results on four nested benchmarks (ACE04, ACE05, GENIA, KBP17) and on four flat ones: the English datasets CoNLL 2003 and OntoNotes 5.0, and the Chinese datasets MSRA and OntoNotes 4.0. This suggests that a single query-driven model can replace task-specific taggers. Code and data are released at github.com/ShannonAI/mrc-for-flat-nested-ner.

Key points

  • Nested NER F1 gains over the previous SOTA: +1.28 on ACE04, +2.55 on ACE05, +5.44 on GENIA and +6.37 on KBP17.
  • Flat NER F1 gains:
    • +0.24 on CoNLL 2003 and +1.95 on OntoNotes 5.0 (English).
    • +0.21 on MSRA (Chinese).
    • +1.49 on Chinese OntoNotes 4.0, where F1 rose from 79.16 to 82.11.
  • Query design matters. Queries built from annotation guideline notes beat other options: label indexes, single keywords, templates, Wikipedia definitions and synonyms. Index-only queries were the only variant where BERT-MRC failed to beat BERT-Tagger.
  • The gain is not just BERT pretraining. Non-pretrained MRC models (QAnet, BiDAF) still outperformed an LSTM-CRF tagger on OntoNotes 5.0.
  • Better transfer and data efficiency:
    • Trained on CoNLL03 and tested on OntoNotes5.0, where 15 of the 18 types are unseen, BERT-MRC reached 72.34 F1 versus 31.87 for BERT-tagger.
    • On Chinese OntoNotes 4.0, BERT-MRC matched BERT-tagger using roughly half the training data.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Natural language processing

Topics: Named entity recognition · Part-of-speech tagging

Cited by

189 papers of our library cite it (of the 140673 whose bibliographies we read); 8 of them have a page here, 181 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2020.acl-main.519

@inproceedings{li2020unified,
  title = {{A Unified MRC Framework for Named Entity Recognition}},
  author = {Li, Xiaoya and Feng, Jingrong and Meng, Yuxian and Han, Qinghong and Wu, Fei and Li, Jiwei},
  booktitle = {Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
  year = {2020},
  doi = {10.18653/v1/2020.acl-main.519},
  url = {https://doi.org/10.18653/v1/2020.acl-main.519}
}

References

58 references, 16 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Beatrice Alex, Barry Haddow, and Claire Grover. 2007. Recognising nested named entities in biomedical text. In Proceedings of the Workshop on BioNLP 2007: Biological, Translational, and Clinical Language Processing, pages 65–72. Association for Computational Linguistics. find
  2. Kate Byrne. 2007. Nested named entity recognition in historical archive text. In International Conference on Semantic Computing (ICSC 2007), pages 589– 596. IEEE. find
  3. Danqi Chen, Adam Fisch, Jason Weston, and Antoine Bordes. 2017. Reading wikipedia to answer open-domain questions. arXiv preprint arXiv:1704.00051. find our card
  4. Jason PC Chiu and Eric Nichols. 2015. Named entity recognition with bidirectional lstm-cnns. arXiv preprint arXiv:1511.08308. find our card
  5. Jason PC Chiu and Eric Nichols. 2016. Named entity recognition with bidirectional lstm-cnns. Transactions of the Association for Computational Linguistics, 4:357–370. find our card
  6. Julie Medero Christopher Walker, Stephanie Strassel and Kazuaki Maeda. 2006. find
  7. Ace 2005 multilingual training corpus. Linguistic Data Consortium, Philadelphia 57. find
  8. Kevin Clark, Minh-Thang Luong, Christopher D. Manning, and Quoc V. Le. 2018. Semi-supervised sequence modeling with cross-view training. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Procfessing, Brussels, Belgium, October 31 - November 4, 2018, pages 1914– 1925. find our card
  9. Ronan Collobert, Jason Weston, Léon Bottou, Michael Karlen, Koray Kavukcuoglu, and Pavel Kuksa. 2011. Natural language processing (almost) from scratch. Journal of Machine Learning Research, 12(Aug):2493–2537. find our card
  10. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805. find
  11. George R Doddington, Alexis Mitchell, Mark A Przybocki, Stephanie M Strassel Lance A Ramshaw, and Ralph M Weischedel. 2005. The automatic content extraction (ace) program-tasks, data, and evaluation. In LREC, 2:1. find
  12. Jenny Rose Finkel and Christopher D Manning. 2009. Nested named entity recognition. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1-Volume 1, pages 141–150. Association for Computational Linguistics. find
  13. Joseph Fisher and Andreas Vlachos. 2019. Merge and label: A novel neural network architecture for nested NER. In Proceedings of the 57th Conference of the Association for Computational Linguistics, ACL 2019, Florence, Italy, July 28- August 2, 2019, Volume 1: Long Papers, pages 5840–5850. find
  14. James Hammerton. 2003. Named entity recognition with long short-term memory. In Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003-Volume 4, pages 172–175. Association for Computational Linguistics. find
  15. Zhiheng Huang, Wei Xu, and Kai Yu. 2015. Bidirectional lstm-crf models for sequence tagging. arXiv preprint arXiv:1508.01991. find our card
  16. Heng Ji, Xiaoman Pan, Boliang Zhang, Joel Nothman, James Mayfield, Paul McNamee, and Cash Costello. 2017. Overview of TAC-KBP2017 13 languages entity discovery and linking. In Proceedings of the 2017 Text Analysis Conference, TAC 2017, Gaithersburg, Maryland, USA, November 13-14, 2017. find
  17. Meizhi Ju, Makoto Miwa, and Sophia Ananiadou. 2018. A neural layered model for nested named entity recognition. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 1446–1459,… find
  18. Arzoo Katiyar and Claire Cardie. 2018. Nested named entity recognition revisited. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 861–871. find
  19. J-D Kim, Tomoko Ohta, Yuka Tateisi, and Jun’ichi Tsujii. 2003. Genia corpus—a semantically annotated corpus for bio-textmining. Bioinformatics, 19(suppl 1):i180–i182. find
  20. John Lafferty, Andrew McCallum, and Fernando CN Pereira. 2001. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. find
  21. Guillaume Lample, Miguel Ballesteros, Sandeep Subramanian, Kazuya Kawakami, and Chris Dyer. 2016. Neural architectures for named entity recognition. arXiv preprint arXiv:1603.01360. find
  22. Gina-Anne Levow. 2006. The third international Chinese language processing bakeoff: Word segmentation and named entity recognition. In Proceedings of the Fifth SIGHAN Workshop on Chinese Language Processing, pages 108–117, Sydney, Australia. Association for Computational Linguistics. find
  23. Omer Levy, Minjoon Seo, Eunsol Choi, and Luke Zettlemoyer. 2017. Zero-shot relation extraction via reading comprehension. arXiv preprint arXiv:1706.04115. find
  24. Xiaoya Li, Fan Yin, Zijun Sun, Xiayu Li, Arianna Yuan, Duo Chai, Mingxin Zhou, and Jiwei Li. 2019. Entity-relation extraction as multi-turn question answering. In Proceedings of the 57th Conference of the Association for Computational Linguistics, ACL 2019, Florence, Italy, July 28- August 2, 2019,… find
  25. Hongyu Lin, Yaojie Lu, Xianpei Han, and Le Sun. 2019a. Sequence-to-nuggets: Nested entity mention detection via anchor-region networks. In Proceedings of the 57th Conference of the Association for Computational Linguistics, ACL 2019, Florence, Italy, July 28- August 2, 2019, Volume 1: Long Papers, p… find
  26. Hongyu Lin, Yaojie Lu, Xianpei Han, and Le Sun. 2019b. Sequence-to-nuggets: Nested entity mention detection via anchor-region networks. In Proceedings of the 57th Conference of the Association for Computational Linguistics, ACL 2019, Florence, Italy, July 28- August 2, 2019, Volume 1: Long Papers, p… find
  27. Wei Lu and Dan Roth. 2015. Joint mention extraction and classification with mention hypergraphs. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 857–867. find
  28. Yi Luan, Dave Wadden, Luheng He, Amy Shah, Mari Ostendorf, and Hannaneh Hajishirzi. 2019. A general framework for information extraction using dynamic span graphs. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Tec… find our card
  29. Xuezhe Ma and Eduard Hovy. 2016. End-to-end sequence labeling via bi-directional lstm-cnns-crf. arXiv preprint arXiv:1603.01354. find our card
  30. Bryan McCann, Nitish Shirish Keskar, Caiming Xiong, and Richard Socher. 2018. The natural language decathlon: Multitask learning as question answering. arXiv preprint arXiv:1806.08730. find our card
  31. Aldrian Obaja Muis and Wei Lu. 2017. Labeling gaps between words: Recognizing overlapping mentions with mention separators. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2608–2618, Copenhagen, Denmark. Association for Computational Linguistics. find
  32. Tomoko Ohta, Yuka Tateisi, and Jin-Dong Kim. 2002. The genia corpus: An annotated research abstract corpus in molecular biology domain. In Proceedings of the Second International Conference on Human Language Technology Research, HLT ’02, pages 82–86, San Francisco, CA, USA. Morgan Kaufmann Publisher… find
  33. Matthew E Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018a. Deep contextualized word representations. arXiv preprint arXiv:1802.05365. find
  34. Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018b. Deep contextualized word representations. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Techn… find
  35. Sameer Pradhan, Mitchell P. Marcus, Martha Palmer, Lance A. Ramshaw, Ralph M. Weischedel, and Nianwen Xue, editors. 2011. Proceedings of the Fifteenth Conference on Computational Natural Language Learning: Shared Task, CoNLL 2011, Portland, Oregon, USA, June 23-24, 2011. ACL. find
  36. Sameer Pradhan, Alessandro Moschitti, Nianwen Xue, Hwee Tou Ng, Anders Björkelund, Olga Uryupina, Yuchen Zhang, and Zhi Zhong. 2013. Towards robust linguistic analysis using OntoNotes. In Proceedings of the Seventeenth Conference on Computational Natural Language Learning, pages 143– 152, Sofia, Bu… find
  37. Pranav Rajpurkar, Robin Jia, and Percy Liang. 2018. Know what you don’t know: Unanswerable questions for squad. arXiv preprint arXiv:1806.03822. find our card
  38. Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016. Squad: 100,000+ questions for machine comprehension of text. arXiv preprint arXiv:1606.05250. find our card
  39. Erik F. Tjong Kim Sang and Fien De Meulder. 2003. Introduction to the conll-2003 shared task: Language-independent named entity recognition. In Proceedings of the Seventh Conference on Natural Language Learning, CoNLL 2003, Held in cooperation with HLT-NAACL 2003, Edmonton, Canada, May 31 - June 1,… find
  40. Cicero Nogueira dos Santos and Victor Guimaraes. 2015. Boosting named entity recognition with neural character embeddings. arXiv preprint arXiv:1505.05008. find
  41. Min Joon Seo, Aniruddha Kembhavi, Ali Farhadi, and Hannaneh Hajishirzi. 2017. Bidirectional attention flow for machine comprehension. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings. find our card
  42. Minjoon Seo, Aniruddha Kembhavi, Ali Farhadi, and Hannaneh Hajishirzi. 2016. Bidirectional attention flow for machine comprehension. arXiv preprint arXiv:1611.01603. find our card
  43. Yelong Shen, Po-Sen Huang, Jianfeng Gao, and Weizhu Chen. 2017. Reasonet: Learning to stop reading in machine comprehension. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 1047–1055. ACM. find
  44. Takashi Shibuya and Eduard H. Hovy. 2019. Nested named entity recognition via second-best sequence learning and decoding. CoRR, abs/1909.02250. find
  45. Mohammad Golam Sohrab and Makoto Miwa. 2018. Deep exhaustive model for nested named entity recognition. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2843–2849, Brussels, Belgium. Association for Computational Linguistics. find
  46. Jana Straková, Milan Straka, and Jan Hajic. 2019. Neural architectures for nested NER through linearization. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5326–5331, Florence, Italy. Association for Computational Linguistics. find
  47. Wei Wu, Yuxian Meng, Qinghong Han, Muyu Li, Xiaoya Li, Jie Mei, Ping Nie, Xiaofei Sun, and Jiwei Li. 2019. Glyce: Glyph-vectors for chinese character representations. arXiv preprint arXiv:1901.10125. — [arXiv:1901.10125](https://arxiv.org/abs/1901.10125) find
  48. Caiming Xiong, Victor Zhong, and Richard Socher. 2016. Dynamic coattention networks for question answering. arXiv preprint arXiv:1611.01604. find our card
  49. Caiming Xiong, Victor Zhong, and Richard Socher. 2017. Dcn+: Mixed objective and deep residual coattention for question answering. arXiv preprint arXiv:1711.00106. — [arXiv:1711.00106](https://arxiv.org/abs/1711.00106) find
  50. Mingbin Xu, Hui Jiang, and Sedtawut Watcharawit-tayakul. 2017. A local detection approach for named entity recognition and mention detection. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), volume 1, pages 1237–1247. find
  51. Adams Wei Yu, David Dohan, Minh-Thang Luong, Rui Zhao, Kai Chen, Mohammad Norouzi, and Quoc V. Le. 2018. Qanet: Combining local convolution with global self-attention for reading comprehension. In 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 -… find our card
  52. Yue Zhang and Jie Yang. 2018. Chinese ner using lattice lstm. arXiv preprint arXiv:1805.02023. find
  53. Changmeng Zheng, Yi Cai, Jingyun Xu, Ho-fung Leung, and Guandong Xu. 2019. A boundary-aware neural model for nested named entity recognition. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Proce… find
  54. Emma Strubell, Patrick Verga, David Belanger, and Andrew McCallum. 2017. Fast and accurate entity recognition with iterated dilated convolutions. arXiv preprint arXiv:1702.02098. find
  55. Charles Sutton, Andrew McCallum, and Khashayar Rohanimanesh. 2007. Dynamic conditional random fields: Factorized probabilistic models for labeling and segmenting sequence data. Journal of Machine Learning Research, 8(Mar):693–723. find
  56. Bailin Wang and Wei Lu. 2018. Neural segmental hypergraphs for overlapping mention recognition. arXiv preprint arXiv:1810.01817. find
  57. Shuohang Wang and Jing Jiang. 2016. Machine comprehension using match-lstm and answer pointer. arXiv preprint arXiv:1608.07905. find our card
  58. Zhiguo Wang, Haitao Mi, Wael Hamza, and Radu Florian. 2016. Multi-perspective context matching for machine comprehension. arXiv preprint arXiv:1612.04211. — [arXiv:1612.04211](https://arxiv.org/abs/1612.04211) find