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.)
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
- 2.
- 3.
- 4.
- 5.
- 6.
- 7.
- 8.
- 9.
- 10.
References
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)
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)
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)
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)
Castano, S., Falduti, M., Ferrara, A., Montanelli, S.: A knowledge-centered framework for exploration and retrieval of legal documents. Inf. Syst. 106, 101842 (2022)
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)
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)
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)
De Cao, N., et al.: Multilingual autoregressive entity linking. Trans. Assoc. Comput. Linguist. 10, 274–290 (2022)
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)
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)
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)
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
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)
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)
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)
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)
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)
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)
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)
McNamee, P., Dang, H.T.: Overview of the tac 2009 knowledge base population track. In: Second Text Analysis Conference (TAC 2009), vol. 2 (2009)
Nothman, J., Ringland, N., Radford, W., Murphy, T., Curran, J.R.: Learning multilingual named entity recognition from Wikipedia. Artif. Intell. 194, 151–175 (2013)
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)
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)
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)
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
Schweter, S.: Italian BERT and Electra models. Zenodo (2020)
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)
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)
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)
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)
Tsai, R.T.H., et al.: Various criteria in the evaluation of biomedical named entity recognition. BMC Bioinform. 7, 1–8 (2006)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc. (2017)
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)
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)
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)
Çetindağ, C., Yazıcıoğlu, B., Koç, A.: Named-entity recognition in Turkish legal texts. Nat. Lang. Eng. 29, 615–642 (2023)
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
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
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)