Elamur Research⌕
☰
home / papers / 2021 / ChEMU-Ref: A Corpus for Modeling Anaphora Resolution in the Chemical Domain

ChEMU-Ref: A Corpus for Modeling Anaphora Resolution in the Chemical Domain

Summary

Building semi-structured knowledge from chemical patents requires tracking how compounds are referenced and changed across a reaction description. Most anaphora corpora either cover only coreference or come from general or biomedical text. This work fills that gap for chemistry. The authors design a domain-specific annotation scheme that combines coreference with four chemistry-motivated bridging sub-types, giving five relation types in total. Using this scheme, chemical experts annotated reaction-description snippets from English patents, drawn from the existing ChEMU corpus. The result is ChEMU-Ref, which shows high inter-annotator agreement. Allowed mentions include chemical names, identifiers, quantified noun phrases, pronouns, and even equipment that implicitly stands for its contents. Discontinuous mentions are also permitted.

For modelling, the authors adapt a general-purpose span-ranking coreference system and split the task into mention detection and relation detection. A model trained jointly on coreference and bridging beats separately trained ones, and embeddings pretrained on chemical text add further gains. The resource matters because it supports richer extraction of reaction processes and became part of the ChEMU 2021 shared task.

Key points

  • ChEMU-Ref is built on snippets from the ChEMU 2020 corpus, where each snippet describes one chemical reaction. It is distributed through the ChEMU 2021 shared task.
  • The scheme uses five anaphora relations: coreference plus four bridging sub-types, including REACTION-ASSOCIATED and WORK-UP.
  • Annotation treats chemical names as atomic, attaches quantities to compound mentions, and allows discontinuous spans. Verbs and event references are excluded.
  • Joint training over coreference and bridging, combined with chemistry-domain pretrained embeddings, gives the strongest results.
  • Error analysis identifies several weaknesses:
    • The model handles only continuous spans.
    • It misses long-distance links.
    • It predicts a single antecedent where several may apply.
    • It struggles with REACTION-ASSOCIATED and WORK-UP prediction.

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: Linguistics & languages

Topics: Coreference resolution

Cited by

15 papers of our library cite it (of the 140673 whose bibliographies we read); 3 of them have a page here, 12 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2021.eacl-main.116

@inproceedings{fang2021chemu,
  title = {{ChEMU-Ref: A Corpus for Modeling Anaphora Resolution in the Chemical Domain}},
  author = {Fang, Biaoyan and Druckenbrodt, Christian and Akhondi, Saber A and He, Jiayuan and Baldwin, Timothy and Verspoor, Karin},
  booktitle = {Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume},
  year = {2021},
  doi = {10.18653/v1/2021.eacl-main.116},
  url = {https://doi.org/10.18653/v1/2021.eacl-main.116}
}

References

51 references, 27 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Saber A Akhondi, Hinnerk Rey, Markus Schwörer, Michael Maier, John Toomey, Heike Nau, Gabriele Ilchmann, Mark Sheehan, Matthias Irmer, Claudia Bobach, Marius Doornenbal, Michelle Gregory, and Jan A Kors. 2019. Automatic identification of relevant chemical compounds from patents. find
  2. Nicholas Asher and Alex Lascarides. 1998. Bridging. Journal of Semantics, 15(1):83–113. find
  3. Michael Bada, Miriam Eckert, Donald Evans, Kristin Garcia, Krista Shipley, Dmitry Sitnikov, William A Baumgartner, K Bretonnel Cohen, Karin Verspoor, Judith A Blake, and Lawrence E Hunter. 2012. Concept annotation in the CRAFT corpus. BMC Bioinformatics, 13(1):161. find
  4. Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015. Neural machine translation by jointly learning to align and translate. In 3rd International Conference on Learning Representations (ICLR 2015), San Diego, USA. find our card
  5. Kevin Clark and Christopher D Manning. 2015. Entity-centric coreference resolution with model stacking. In Proc. of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 1405–… find our card
  6. Kevin Clark and Christopher D. Manning. 2016a. Deep reinforcement learning for mention-ranking coreference models. In Proc. of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 2256–2262, Austin, USA. find our card
  7. Kevin Clark and Christopher D. Manning. 2016b. Improving coreference resolution by learning entity-level distributed representations. In Proc. of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 643–653, Berlin, Germany. find our card
  8. K Bretonnel Cohen, Arrick Lanfranchi, Miji Joo-young Choi, Michael Bada, William A Baumgartner, Natalya Panteleyeva, Karin Verspoor, Martha Palmer, and Lawrence E Hunter. 2017. Coreference annotation and resolution in the colorado richly annotated full text (CRAFT) corpus of biomedical journal artic… find
  9. Biaoyan Fang, Christian Druckenbrodt, Colleen Yeow Hui Shiuan, Sacha Novakovic, Ralph Hössel, Saber A. Akhondi, Jiayuan He, Meladel Mistica, Timothy Baldwin, and Karin Verspoor. 2021. ChEMU-Ref dataset for modeling anaphora resolution in the chemical domain. Mendeley Data. find
  10. Abbas Ghaddar and Philippe Langlais. 2016a. Wikicoref: An English coreference-annotated corpus of Wikipedia articles. In Proc. of the Tenth International Conference on Language Resources and Evaluation (LREC 2016), pages 136–142, Portorož, Slovenia. find our card
  11. Abbas Ghaddar and Phillippe Langlais. 2016b. Wiki-Coref: An English coreference-annotated corpus of Wikipedia articles. In Proc. of the Tenth International Conference on Language Resources and Evaluation (LREC’16), pages 136–142, Portorož, Slovenia. find our card
  12. Loı̈c Grobol. 2019. Neural coreference resolution with limited lexical context and explicit mention detection for oral French. In Proc. of the Second Workshop on Computational Models of Reference, Anaphora and Coreference, pages 8–14, Minneapolis, USA. find our card
  13. P Gwynne and G Heabrer. 2015. Recent developments in drug discovery: Improvements in efficiency. Science. find
  14. Jiayuan He, Biaoyan Fang, Hiyori Yoshikawa, Yuan Li, Saber A Akhondi, Christian Druckenbrodt, Camilo Thorne, Zubair Afzal, Zenan Zhai, Lawrence Cavedon, Trevor Cohn, Timothy Baldwin, and Karin Verspoor. to appear. ChEMU 2021: Reaction reference resolution and anaphora resolution in chemical patents.… find
  15. Jiayuan He, Dat Quoc Nguyen, Saber A. Akhondi, Christian Druckenbrodt, Camilo Thorne, Ralph Hoessel, Zubair Afzal, Zenan Zhai, Biaoyan Fang, Hiyori Yoshikawa, Ameer Albahem, Lawrence Cavedon, Trevor Cohn, Timothy Baldwin, and Karin Verspoor. 2020. Overview of ChEMU 2020: Named entity recognition and… find
  16. Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural Computation, 9(8):1735–1780. find
  17. Yufang Hou. 2018a. A deterministic algorithm for bridging anaphora resolution. In Proc. of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1938–1948, Brussels, Belgium. find our card
  18. Yufang Hou. 2018b. Enhanced word representations for bridging anaphora resolution. In Proc. of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 1–7, New Orleans, USA. find our card
  19. Yufang Hou. 2020. Bridging anaphora resolution as question answering. In Proc. of the 58th Annual Meeting of the Association for Computational Linguistics, pages 1428–1438, Online. find our card
  20. Yufang Hou, Katja Markert, and Michael Strube. 2014. A rule-based system for unrestricted bridging resolution: Recognizing bridging anaphora and finding links to antecedents. In Proc. of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP 2014), pages 2082– 2093, Doha, Qat… find our card
  21. Yufang Hou, Katja Markert, and Michael Strube. 2018. Unrestricted bridging resolution. Computational Linguistics, 44(2):237–284. find our card
  22. Ben Kantor and Amir Globerson. 2019. Coreference resolution with entity equalization. In Proc. of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019), pages 673–677, Florence, Italy. find our card
  23. Jin-Dong Kim, Ngan Nguyen, Yue Wang, Jun’ichi Tsujii, Toshihisa Takagi, and Akinori Yonezawa. 2012. The Genia event and protein coreference tasks of the BioNLP shared task 2011. BMC Bioinformatics, 13(11):S1. find
  24. Klaus Krippendorff. 2004. Measuring the reliability of qualitative text analysis data. Quality and quantity, 38:787–800. find
  25. Kenton Lee, Luheng He, Mike Lewis, and Luke Zettlemoyer. 2017. End-to-end neural coreference resolution. In Proc. of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 188–197, Copenhagen, Denmark. find our card
  26. Kenton Lee, Luheng He, and Luke Zettlemoyer. 2018. Higher-order coreference resolution with coarse-to-fine inference. In Proc. of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 687–692, N… find our card
  27. Jiao Li, Yueping Sun, Robin J Johnson, Daniela Sci-aky, Chih-Hsuan Wei, Robert Leaman, Allan Peter Davis, Carolyn J Mattingly, Thomas C Wiegers, and Zhiyong Lu. 2016. BioCreative V CDR task corpus: a resource for chemical disease relation extraction. find
  28. Xiaoqiang Luo. 2005. On coreference resolution performance metrics. In Proc. of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing (EMNLP 2005), pages 25–32, Vancouver, Canada. find our card
  29. Nafise Sadat Moosavi and Michael Strube. 2016. Which coreference evaluation metric do you trust? a proposal for a link-based entity aware metric. In Proc. of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 632–642, Berlin, Germany. find our card
  30. Vinod Nair and Geoffrey E Hinton. 2010. Rectified linear units improve restricted Boltzmann machines. In Proc. of the 33rd International Conference on Machine Learning (ICML 2016), New York, USA. find
  31. Vincent Ng. 2017. Machine learning for entity coreference resolution: A retrospective look at two decades of research. In Proc. of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI’17), pages 4877–4884, San Francisco, USA. find
  32. Ngan Nguyen, Jin-Dong Kim, and Jun’ichi Tsujii. 2011. Overview of BioNLP 2011 protein coreference shared task. In Proc. of BioNLP Shared Task 2011 Workshop, pages 74–82, Portland, USA. find our card
  33. Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014. GloVe: Global vectors for word representation. In Proc. of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1532–1543, Doha, Qatar. find
  34. Matthew Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018. Deep contextualized word representations. In Proc. of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, V… find
  35. Massimo Poesio, Ron Artstein, et al. 2008. Anaphoric annotation in the ARRAU corpus. In Proc. of the Sixth International Conference on Language Resources and Evaluation (LREC 2008), Marrakech, Morocco. find our card
  36. Massimo Poesio, Roland Stuckardt, and Yannick Versley. 2016. Anaphora Resolution. Springer. find
  37. Sameer Pradhan, Alessandro Moschitti, Nianwen Xue, Olga Uryupina, and Yuchen Zhang. 2012. CoNLL-2012 shared task: Modeling multilingual unrestricted coreference in OntoNotes. In Proc. of EMNLP-CoNLL 2012: Conference on Empirical Methods in Natural Language Processing and Computational Natural Langua… find our card
  38. Marta Recasens and Eduard Hovy. 2011. BLANC: Implementing the Rand index for coreference evaluation. Natural Language Engineering, 17(4):485– 510. find
  39. Ina Rösiger. 2016. Scicorp: A corpus of English scientific articles annotated for information status analysis. In Proc. of the Tenth International Conference on Language Resources and Evaluation (LREC 2016), pages 1743–1749, Portorož, Slovenia. find our card
  40. Ina Rösiger. 2018a. BASHI: A corpus of Wall Street Journal articles annotated with bridging links. In Proc. of the Eleventh International Conference on Language Resources and Evaluation (LREC-2018). find our card
  41. Ina Rösiger. 2018b. Rule- and learning-based methods for bridging resolution in the ARRAU corpus. In Proc. of the First Workshop on Computational Models of Reference, Anaphora and Coreference, pages 23–33, New Orleans, USA. find our card
  42. Ina Rösiger. 2019. Computational modelling of coreference and bridging resolution. Ph.D. thesis, Stuttgart University. find
  43. Ina Rösiger, Arndt Riester, and Jonas Kuhn. 2018. Bridging resolution: Task definition, corpus resources and rule-based experiments. In Proc. of the 27th International Conference on Computational Linguistics, pages 3516–3528, Santa Fe, USA. find our card
  44. Karin Verspoor, Dat Quoc Nguyen, Saber A. Akhondi, Christian Druckenbrodt, Camilo Thorne, Ralph Hoessel, Jiayuan He, and Zenan Zhai. 2020. ChEMU dataset for information extraction from chemical patents. Mendeley Data. find
  45. Ralph Weischedel, Martha Palmer, Mitchell Marcus, Eduard Hovy, Sameer Pradhan, Lance Ramshaw, Nianwen Xue, Ann Taylor, Jeff Kaufman, Michelle Franchini, Mohammed El-Bachouti, Robert Belvin, and Ann Houston. 2013. OntoNotes release 5.0. Linguistic Data Consortium Catalog No. LDC2013T19. find
  46. Sam Wiseman, Alexander M. Rush, Stuart Shieber, and Jason Weston. 2015. Learning anaphoricity and antecedent ranking features for coreference resolution. In Proc. of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Languag… find our card
  47. Sam Wiseman, Alexander M. Rush, and Stuart M. Shieber. 2016. Learning global features for coreference resolution. In Proc. of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 994–1004, San Diego, USA. find our card
  48. Juntao Yu and Massimo Poesio. 2020. Multi-task learning based neural bridging reference resolution. arXiv preprint arXiv:2003.03666. — [arXiv:2003.03666](https://arxiv.org/abs/2003.03666) find
  49. Amir Zeldes. 2017. The GUM corpus: Creating multilayer resources in the classroom. Language Resources and Evaluation, 51(3):581–612. find
  50. Zenan Zhai, Dat Quoc Nguyen, Saber Akhondi, Camilo Thorne, Christian Druckenbrodt, Trevor Cohn, Michelle Gregory, and Karin Verspoor. 2019. Improving chemical named entity recognition in patents with contextualized word embeddings. In Proc. of the 18th BioNLP Workshop and Shared Task, pages 328–338,… find
  51. Rui Zhang, Cı́cero Nogueira dos Santos, Michihiro Yasunaga, Bing Xiang, and Dragomir Radev. 2018. Neural coreference resolution with deep biaffine attention by joint mention detection and mention clustering. In Proc. of the 56th Annual Meeting of the Association for Computational Linguistics (Volume… find our card

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/papers/fang-2021-chemu-ref-corpus-modeling-anaphora/