Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Named Entity Recognition and Linking for Entity Extraction from Italian Civil Judgements

  • Conference paper
  • First Online:
AIxIA 2023 – Advances in Artificial Intelligence (AIxIA 2023)

Abstract

The extraction of named entities from court judgments is useful in several downstream applications, such as document anonymization and semantic search engines. In this paper, we discuss the application of named entity recognition and linking (NEEL) to extract entities from Italian civil court judgments. To develop and evaluate our work, we use a corpus of 146 manually annotated court judgments. We use a pipeline that combines a transformer-based Named Entity Recognition (NER) component, a transformer-based Named Entity Linking (NEL) component, and a NIL prediction component. While the NEL and NIL prediction components are not fine-tuned on domain-specific data, the NER component is fine-tuned on the annotated corpus. In addition, we compare different masked language modeling (MLM) adaptation strategies to optimize the result and investigate their impact. Results obtained on a 30-document test set reveal satisfactory performance, especially on the NER task, and emphasize challenges to improve NEEL on similar documents. Our code is available on GitHub.(https://github.com/rpo19/pozzi_aixia_2023. We are not allowed to publish sensitive data and the NER models trained on sensitive data.)

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 79.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    https://yago-knowledge.org/.

  2. 2.

    http://1641.tcd.ie/.

  3. 3.

    https://spacy.io.

  4. 4.

    https://www.dbpedia.org/.

  5. 5.

    https://tac.nist.gov/.

  6. 6.

    https://spacy.io/universe/project/spacy-transformers.

  7. 7.

    https://huggingface.co/dbmdz/bert-base-italian-xxl-cased.

  8. 8.

    https://huggingface.co/dlicari/Italian-Legal-BERT-SC.

  9. 9.

    https://huggingface.co/dbmdz/bert-base-italian-uncased.

  10. 10.

    https://it.wikipedia.org.

References

  1. Ayoola, T., Tyagi, S., Fisher, J., Christodoulopoulos, C., Pierleoni, A.: ReFinED: an efficient zero-shot-capable approach to end-to-end entity linking. In: Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Track. Association for Computational Linguistics (2022)

    Google Scholar 

  2. Basile, P., Caputo, A., Gentile, A.L., Rizzo, G.: Overview of the EVALITA 2016 named entity recognition and linking in Italian tweets (NEEL-IT) task. In: of the Final Workshop, vol. 7 (2016)

    Google Scholar 

  3. Batini, C., Bellandi, V., Ceravolo, P., Moiraghi, F., Palmonari, M., Siccardi, S.: Semantic data integration for investigations: lessons learned and open challenges. In: 2021 IEEE International Conference on Smart Data Services (SMDS) (2021)

    Google Scholar 

  4. Cardellino, C., Teruel, M., Alemany, L.A., Villata, S.: A low-cost, high-coverage legal named entity recognizer, classifier and linker. In: Proceedings of the 16th Edition of the International Conference on Artificial Intelligence and Law. ICAIL 2017, Association for Computing Machinery (2017)

    Google Scholar 

  5. Castano, S., Falduti, M., Ferrara, A., Montanelli, S.: A knowledge-centered framework for exploration and retrieval of legal documents. Inf. Syst. 106, 101842 (2022)

    Article  Google Scholar 

  6. Catelli, R., Gargiulo, F., Casola, V., De Pietro, G., Fujita, H., Esposito, M.: Crosslingual named entity recognition for clinical de-identification applied to a COVID-19 Italian data set. Appl. Soft Comput. 97, 106779 (2020)

    Article  Google Scholar 

  7. Chalkidis, I., Fergadiotis, M., Malakasiotis, P., Aletras, N., Androutsopoulos, I.: LEGAL-BERT: the Muppets straight out of law school. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics (2020)

    Google Scholar 

  8. Daiber, J., Jakob, M., Hokamp, C., Mendes, P.N.: Improving efficiency and accuracy in multilingual entity extraction. In: Proceedings of the 9th International Conference on Semantic Systems. I-SEMANTICS 2013, Association for Computing Machinery (2013)

    Google Scholar 

  9. De Cao, N., et al.: Multilingual autoregressive entity linking. Trans. Assoc. Comput. Linguist. 10, 274–290 (2022)

    Article  Google Scholar 

  10. Devlin, J., Chang, M.W., 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). Association for Computational Linguistics (2019)

    Google Scholar 

  11. Elnaggar, A., Otto, R., Matthes, F.: Deep learning for named-entity linking with transfer learning for legal documents. In: Proceedings of the 2018 Artificial Intelligence and Cloud Computing Conference. AICCC 2018, Association for Computing Machinery (2018)

    Google Scholar 

  12. He, Z., Liu, S., Li, M., Zhou, M., Zhang, L., Wang, H.: Learning entity representation for entity disambiguation. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Association for Computational Linguistics (2013)

    Google Scholar 

  13. Heist, N., Paulheim, H.: NASTyLinker: NIL-aware scalable transformer-based entity linker. In: Pesquita, C., et al. (eds.) The Semantic Web, ESWC 2023. Lecture Notes in Computer Science, vol. 13870, pp. 174–191. Springer, Switzerland (2023). https://doi.org/10.1007/978-3-031-33455-9_11

    Chapter  Google Scholar 

  14. Humeau, S., Shuster, K., Lachaux, M.A., Weston, J.: Poly-encoders: Architectures and pre-training strategies for fast and accurate multi-sentence scoring. In: International Conference on Learning Representations (2019)

    Google Scholar 

  15. Kassner, N., Petroni, F., Plekhanov, M., Riedel, S., Cancedda, N.: EDIN: an end-to-end benchmark and pipeline for unknown entity discovery and indexing. In: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics (2022)

    Google Scholar 

  16. Keshavarz, H., et al.: Named entity recognition in long documents: an end-to-end case study in the legal domain. In: 2022 IEEE International Conference on Big Data (Big Data) (2022)

    Google Scholar 

  17. Klie, J.C., Eckart de Castilho, R., Gurevych, I.: From zero to hero: human-in-the-loop entity linking in low resource domains. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics (2020)

    Google Scholar 

  18. Kolitsas, N., Ganea, O.E., Hofmann, T.: End-to-end neural entity linking. In: Proceedings of the 22nd Conference on Computational Natural Language Learning. Association for Computational Linguistics (2018)

    Google Scholar 

  19. Lafferty, J.D., McCallum, A., Pereira, F.C.N.: Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proceedings of the Eighteenth International Conference on Machine Learning. ICML 2001, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001)

    Google Scholar 

  20. Licari, D., Comandè, G.: ITALIAN-LEGAL-BERT: a pre-trained transformer language model for Italian law. In: Companion Proceedings of the 23rd International Conference on Knowledge Engineering and Knowledge Management. CEUR Workshop Proceedings, vol. 3256. CEUR (2022)

    Google Scholar 

  21. McNamee, P., Dang, H.T.: Overview of the tac 2009 knowledge base population track. In: Second Text Analysis Conference (TAC 2009), vol. 2 (2009)

    Google Scholar 

  22. Nothman, J., Ringland, N., Radford, W., Murphy, T., Curran, J.R.: Learning multilingual named entity recognition from Wikipedia. Artif. Intell. 194, 151–175 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  23. Aprosio, A.P., Moretti, G.: Tint 2.0: an all-inclusive suite for NLP in Italian. In: Proceedings of the Fifth Italian Conference on Computational Linguistics CLiC-it, vol. 10 (2018)

    Google Scholar 

  24. Pozzi, R., Moiraghi, F., Lodi, F., Palmonari, M.: Evaluation of incremental entity extraction with background knowledge and entity linking. In: Proceedings of the 11th International Joint Conference on Knowledge Graphs. IJCKG 2022, Association for Computing Machinery (2023)

    Google Scholar 

  25. Procopio, L., Conia, S., Barba, E., Navigli, R.: Entity disambiguation with entity definitions. In: Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics. Association for Computational Linguistics (2023)

    Google Scholar 

  26. Rosales-Méndez, H., Hogan, A., Poblete, B.: VoxEL: a benchmark dataset for multilingual entity linking. In: Vrandečić, D., et al. (eds.) ISWC 2018. LNCS, vol. 11137, pp. 170–186. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00668-6_11

    Chapter  Google Scholar 

  27. Schweter, S.: Italian BERT and Electra models. Zenodo (2020)

    Google Scholar 

  28. Sevgili, O., Shelmanov, A., Arkhipov, M.V., Panchenko, A., Biemann, C.: Neural entity linking: a survey of models based on deep learning. Semant. Web 13, 527–570 (2020)

    Article  Google Scholar 

  29. Tamper, M., Oksanen, A., Tuominen, J., Hietanen, A., Hyvönen, E.: Automatic annotation service APPI: named entity linking in legal domain. In: The Semantic Web: ESWC 2020 Satellite Events. Springer International Publishing (2020)

    Google Scholar 

  30. Tedeschi, S., Navigli, R.: MultiNERD: a multilingual, multi-genre and fine-grained dataset for named entity recognition (and disambiguation). In: Findings of the Association for Computational Linguistics: NAACL 2022. Association for Computational Linguistics (2022)

    Google Scholar 

  31. Sang, E.F.T.K., De Meulder, 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 (2003)

    Google Scholar 

  32. Tsai, R.T.H., et al.: Various criteria in the evaluation of biomedical named entity recognition. BMC Bioinform. 7, 1–8 (2006)

    Article  Google Scholar 

  33. Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc. (2017)

    Google Scholar 

  34. Wang, X., et al.: Automated concatenation of embeddings for structured prediction. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (2021)

    Google Scholar 

  35. Wu, L., Petroni, F., Josifoski, M., Riedel, S., Zettlemoyer, L.: Scalable zero-shot entity linking with dense entity retrieval. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics (2020)

    Google Scholar 

  36. Yamada, I., Shindo, H., Takeda, H., Takefuji, Y.: Joint learning of the embedding of words and entities for named entity disambiguation. In: Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning. Association for Computational Linguistics (2016)

    Google Scholar 

  37. Çetindağ, C., Yazıcıoğlu, B., Koç, A.: Named-entity recognition in Turkish legal texts. Nat. Lang. Eng. 29, 615–642 (2023)

    Article  Google Scholar 

Download references

Acknowledgements

This research has been partially funded by Cini in the context of the Italian project Datalake@Giustizia and by the project PON Next Generation UPP promoted by the Italian Ministry of Justice.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Riccardo Pozzi .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Pozzi, R., Rubini, R., Bernasconi, C., Palmonari, M. (2023). Named Entity Recognition and Linking for Entity Extraction from Italian Civil Judgements. In: Basili, R., Lembo, D., Limongelli, C., Orlandini, A. (eds) AIxIA 2023 – Advances in Artificial Intelligence. AIxIA 2023. Lecture Notes in Computer Science(), vol 14318. Springer, Cham. https://doi.org/10.1007/978-3-031-47546-7_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-47546-7_13

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-47545-0

  • Online ISBN: 978-3-031-47546-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics