research-article

Deep Learning for Named-Entity Linking with Transfer Learning for Legal Documents

Authors:

Ahmed Elnaggar,

Florian MatthesAuthors Info & Claims

AICCC '18: Proceedings of the 2018 Artificial Intelligence and Cloud Computing Conference

Pages 23 - 28

https://doi.org/10.1145/3299819.3299846

Published: 21 December 2018 Publication History

Abstract

In the legal domain it is important to differentiate between words in general, and afterwards to link the occurrences of the same entities. The topic to solve these challenges is called Named-Entity Linking (NEL). Current supervised neural networks designed for NEL use publicly available datasets for training and testing. However, this paper focuses especially on the aspect of applying transfer learning approach using networks trained for NEL to legal documents. Experiments show consistent improvement in the legal datasets that were created from the European Union law in the scope of this research. Using transfer learning approach, we reached F1-score of 98.90% and 98.01% on the legal small and large test dataset.

References

[1]

Cristian Cardellino, Milagro Teruel, Laura Alonso Alemany, and Serena Villata. 2017. A low-cost, high-coverage legal named entity recognizer, classifier and linker. In Proceedings of the 16th edition of the International Conference on Articial Intelligence and Law. ACM, 9--18.

Digital Library

[2]

Silviu Cucerzan. 2007. Large-scale named entity disambiguation based on Wikipedia data. In Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL).

[3]

Leon Derczynski, Diana Maynard, Giuseppe Rizzo, Marieke van Erp, Genevieve Gorrell, Raphaël Troncy, Johann Petrak, and Kalina Bontcheva. 2015. Analysis of named entity recognition and linking for tweets. Information Processing & Management 51, 2 (2015), 32--49.

[4]

Sanda Erdelez and Sheila O'Hare. 1997. Legal informatics: application of information technology in law. Annual Review of Information Science and Technology (ARIST) 32 (1997), 367--402.

[5]

Octavian-Eugen Ganea and Thomas Hofmann. 2017. Deep joint entity disambiguation with local neural attention. arXiv preprint arXiv:1704.04920 (2017).

[6]

Yoav Goldberg. 2016. A primer on neural network models for natural language processing. Journal of Artificial Intelligence Research 57 (2016), 345--420.

Digital Library

[7]

Zhaochen Guo and Denilson Barbosa. 2017. WNED datasets and results.

[8]

Leonard Heilig, Silvia Schwarze, and Stefan Voß. 2017. An analysis of digital transformation in the history and future of modern ports. (2017).

[9]

Johannes Hoffart, Mohamed Amir Yosef, Ilaria Bordino, Hagen Fürstenau, Manfred Pinkal, Marc Spaniol, Bilyana Taneva, Stefan Thater, and Gerhard Weikum. 2011. Robust disambiguation of named entities in text. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 782--792.

Digital Library

[10]

Johannes Hoffart, Mohamed Amir Yosef, Ilaria Bordino, Hagen Fürstenau, Manfred Pinkal, Marc Spaniol, Bilyana Taneva, Stefan Thater, and Gerhard Weikum. 2011. Robust Disambiguation of Named Entities in Text. In Conference on Empirical Methods in Natural Language Processing, EMNLP 2011, Edinburgh, Scotland. 782--792.

Digital Library

[11]

M Hossin and MN Sulaiman. 2015. A review on evaluation metrics for data classification evaluations. International Journal of Data Mining & Knowledge Management Process 5, 2 (2015), 1.

[12]

Junlin Hu, Jiwen Lu, and Yap-Peng Tan. 2015. Deep transfer metric learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 325--333.

[13]

Sinno Jialin Pan, Qiang Yang, et al. 2010. A survey on transfer learning. IEEE Transactions on knowledge and data engineering 22, 10 (2010), 1345--1359.

Digital Library

[14]

Lev Ratinov, Dan Roth, Doug Downey, and Mike Anderson. 2011. Local and global algorithms for disambiguation to wikipedia. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1. Association for Computational Linguistics, 1375--1384.

Digital Library

[15]

Michael T Rosenstein, Zvika Marx, Leslie Pack Kaelbling, and Thomas G Dietterich. 2005. To transfer or not to transfer. In NIPS 2005 workshop on transfer learning, Vol. 898. 1--4.

[16]

Yoshihide Sawada, Yoshikuni Sato, Toru Nakada, Kei Ujimoto, and Nobuhiro Hayashi. 2017. All-transfer learning for deep neural networks and its application to sepsis classification. arXiv preprint arXiv:1711.04450 (2017).

[17]

Wei Shen, Jianyong Wang, and Jiawei Han. 2015. Entity linking with a knowledge base: Issues, techniques, and solutions. IEEE Transactions on Knowledge and Data Engineering 27, 2 (2015), 443--460.

[18]

Avirup Sil, Ernest Cronin, Penghai Nie, Yinfei Yang, Ana-Maria Popescu, and Alexander Yates. 2012. Linking named entities to any database. In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. Association for Computational Linguistics, 116--127.

Digital Library

[19]

Avirup Sil and Radu Florian. 2017. One for all: Towards language independent named entity linking. arXiv preprint arXiv:1712.01797 (2017).

[20]

Karl Weiss, Taghi M Khoshgoftaar, and DingDing Wang. 2016. A survey of transfer learning. Journal of Big Data 3, 1 (2016), 9.

Cited By

Krasadakis PSakkopoulos EVerykios V(2024)A Survey on Challenges and Advances in Natural Language Processing with a Focus on Legal Informatics and Low-Resource LanguagesElectronics10.3390/electronics1303064813:3(648)Online publication date: 4-Feb-2024
https://doi.org/10.3390/electronics13030648
Quevedo ECerny TRodriguez ARivas PYero JSooksatra KZhakubayev ATaibi D(2024)Legal Natural Language Processing From 2015 to 2022: A Comprehensive Systematic Mapping Study of Advances and ApplicationsIEEE Access10.1109/ACCESS.2023.333394612(145286-145317)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2023.3333946
Zhang HSun YXu BLin H(2024)LegalATLE: an active transfer learning framework for legal triple extractionApplied Intelligence10.1007/s10489-024-05842-yOnline publication date: 2-Oct-2024
https://doi.org/10.1007/s10489-024-05842-y
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Index Terms

Deep Learning for Named-Entity Linking with Transfer Learning for Legal Documents
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
  2. Machine learning
    1. Learning paradigms
      1. Multi-task learning
        Transfer learning

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cover image ACM Other conferences

AICCC '18: Proceedings of the 2018 Artificial Intelligence and Cloud Computing Conference

December 2018

206 pages

ISBN:9781450366236

DOI:10.1145/3299819

Copyright © 2018 ACM.

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Publication History

Published: 21 December 2018

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AICCC '18

AICCC '18: 2018 Artificial Intelligence and Cloud Computing Conference

December 21 - 23, 2018

Tokyo, Japan

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Cited By

Krasadakis PSakkopoulos EVerykios V(2024)A Survey on Challenges and Advances in Natural Language Processing with a Focus on Legal Informatics and Low-Resource LanguagesElectronics10.3390/electronics1303064813:3(648)Online publication date: 4-Feb-2024
https://doi.org/10.3390/electronics13030648
Quevedo ECerny TRodriguez ARivas PYero JSooksatra KZhakubayev ATaibi D(2024)Legal Natural Language Processing From 2015 to 2022: A Comprehensive Systematic Mapping Study of Advances and ApplicationsIEEE Access10.1109/ACCESS.2023.333394612(145286-145317)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2023.3333946
Zhang HSun YXu BLin H(2024)LegalATLE: an active transfer learning framework for legal triple extractionApplied Intelligence10.1007/s10489-024-05842-yOnline publication date: 2-Oct-2024
https://doi.org/10.1007/s10489-024-05842-y
Bozdag MSevim NKoç A(2023)Measuring and Mitigating Gender Bias in Legal Contextualized Language ModelsACM Transactions on Knowledge Discovery from Data10.1145/362860218:4(1-26)Online publication date: 18-Oct-2023
https://dl.acm.org/doi/10.1145/3628602
Pozzi RRubini RBernasconi CPalmonari M(2023)Named Entity Recognition and Linking for Entity Extraction from Italian Civil JudgementsAIxIA 2023 – Advances in Artificial Intelligence10.1007/978-3-031-47546-7_13(187-201)Online publication date: 2-Nov-2023
https://doi.org/10.1007/978-3-031-47546-7_13
Wei BKuang KSun CFeng JZhang YZhu XZhou JZhai YWu F(2022)A full-process intelligent trial system for smart court一种智慧法院的全流程智能化审判系统Frontiers of Information Technology & Electronic Engineering10.1631/FITEE.210004123:2(186-206)Online publication date: 18-Mar-2022
https://doi.org/10.1631/FITEE.2100041
Sevim NŞahinuç FKoç A(2022)Gender bias in legal corpora and debiasing itNatural Language Engineering10.1017/S1351324922000122(1-34)Online publication date: 30-Mar-2022
https://doi.org/10.1017/S1351324922000122
Sansone CSperlí G(2022)Legal Information Retrieval systemsInformation Systems10.1016/j.is.2021.101967106:COnline publication date: 12-May-2022
https://dl.acm.org/doi/10.1016/j.is.2021.101967
Krasadakis PSakkopoulos EVerykios V(2021)A Natural Language Processing Survey on Legislative and Greek DocumentsProceedings of the 25th Pan-Hellenic Conference on Informatics10.1145/3503823.3503898(407-412)Online publication date: 26-Nov-2021
https://dl.acm.org/doi/10.1145/3503823.3503898
Zhang HPan BLi R(2021)Legal Judgment Elements Extraction Approach with Law Article-aware MechanismACM Transactions on Asian and Low-Resource Language Information Processing10.1145/348524421:3(1-15)Online publication date: 21-Dec-2021
https://dl.acm.org/doi/10.1145/3485244
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