Nothing Special   »   [go: up one dir, main page]

Skip to main content
Log in

SM-BERT-CR: a deep learning approach for case law retrieval with supporting model

  • Original Research
  • Published:
Artificial Intelligence and Law Aims and scope Submit manuscript

Abstract

Case law retrieval is the task of locating truly relevant legal cases given an input query case. Unlike information retrieval for general texts, this task is more complex with two phases (legal case retrieval and legal case entailment) and much harder due to a number of reasons. First, both the query and candidate cases are long documents consisting of several paragraphs. This makes it difficult to model with representation learning that usually has restriction on input length. Second, the concept of relevancy in this domain is defined based on the legal relation that goes beyond the lexical or topical relevance. This is a real challenge because normal text matching will not work. Third, building a large and accurate legal case dataset requires a lot of effort and expertise. This is obviously an obstacle to creating enough data for training deep retrieval models. In this paper, we propose a novel approach called supporting model that can deal with both phases. The underlying idea is the case–case supporting relation and the paragraph–paragraph as well as the decision-paragraph matching strategy. In addition, we propose a method to automatically create a large weak-labeling dataset to overcome the lack of data. The experiments showed that our solution has achieved the state-of-the-art results for both case retrieval and case entailment phases.

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

Access this article

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

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Notes

  1. https://ca.vlex.com/.

  2. https://sites.ualberta.ca/~rabelo/COLIEE2020/.

References

  • Arora S, Liang Y, Ma T (2017) A simple but tough-to-beat baseline for sentence embeddings. In: 5th international conference on learning representations, ICLR 2017

  • Bench-Capon T, Araszkiewicz M, Ashley K, Atkinson K, Bex F, Borges F, Bourcier D, Bourgine P, Conrad JG, Francesconi E et al (2012) A history of AI and law in 50 papers: 25 years of the International Conference on AI and Law. Artif Intell Law 20(3):215–319

    Article  Google Scholar 

  • Berger A, Lafferty J (2017) Information retrieval as statistical translation, vol 51. ACM SIGIR Forum, ACM New York, pp 219–226

  • Bhattacharya P, Ghosh K, Ghosh S, Pal A, Mehta P, Bhattacharya A, Majumder P (2019) Overview of the fire 2019 aila track: artificial intelligence for legal assistance. In: FIRE (Working Notes), pp 1–12

  • Burges C, Shaked T, Renshaw E, Lazier A, Deeds M, Hamilton N, Hullender G (2005) Learning to rank using gradient descent. In: Proceedings of the 22nd international conference on machine learning, pp 89–96

  • Burges CJ, Ragno R, Le QV (2007) Learning to rank with nonsmooth cost functions. In: Advances in neural information processing systems, pp 193–200

  • Castells P, Fernandez M, Vallet D (2006) An adaptation of the vector-space model for ontology-based information retrieval. IEEE Trans Knowl Data Eng 19(2):261–272

    Article  Google Scholar 

  • Dai Z, Callan J (2019) Deeper text understanding for IR with contextual neural language modeling. In: Proceedings of the 42nd international ACM SIGIR conference on research and development in information retrieval, pp 985–988

  • Devlin J, Chang MW, Lee K, Toutanova K (2019a) BERT: Pre-training of deep bidirectional transformers for language understanding. In: NAACL, pp 4171–4186

  • Devlin J, Chang MW, Lee K, Toutanova K (2019b) Bert: pre-training of deep bidirectional transformers for language understanding. In: NAACL-HLT

  • Gao J, Pantel P, Gamon M, He X, Deng L (2014) Modeling interestingness with deep neural networks. In: EMNLP

  • Guo J, Fan Y, Pang L, Yang L, Ai Q, Zamani H, Wu C, Croft WB, Cheng X (2020) A deep look into neural ranking models for information retrieval. Inf Process Manag 57(6):102067

    Article  Google Scholar 

  • Hu B, Lu Z, Li H, Chen Q (2014) Convolutional neural network architectures for matching natural language sentences. In: Advances in neural information processing systems, pp 2042–2050

  • Huang PS, He X, Gao J, Deng L, Acero A, Heck L (2013) Learning deep structured semantic models for web search using clickthrough data. In: Proceedings of the 22nd ACM international conference on Information & Knowledge Management, pp 2333–2338

  • Kingma D, Ba J (2014) Adam: a method for stochastic optimization. In: International conference on learning representations

  • Liu TY (2009) Learning to rank for information retrieval. Found Trends Inf Retr 3:225–331

    Article  Google Scholar 

  • Mandal A, Chaki R, Saha S, Ghosh K, Pal A, Ghosh S (2017) Measuring similarity among legal court case documents. In: Proceedings of the 10th annual ACM India compute conference, pp 1–9

  • Marchesin S, Purpura A, Silvello G (2020) Focal elements of neural information retrieval models. An outlook through a reproducibility study. Inf Process Manag 57(6):102109

    Article  Google Scholar 

  • Mihalcea R, Tarau P (2004) Textrank: bringing order into text. In: Proceedings of the 2004 conference on empirical methods in natural language processing, pp 404–411

  • Mitra B, Diaz F, Craswell N (2017) Learning to match using local and distributed representations of text for web search. In: Proceedings of the 26th international conference on world wide web, pp 1291–1299

  • Oard DW, Webber W (2013) Information retrieval for e-discovery. Inf Retr 7(2–3):99–237

    Google Scholar 

  • Palangi H, Deng L, Shen Y, Gao J, He X, Chen J, Song X, Ward R (2016) Deep sentence embedding using long short-term memory networks: analysis and application to information retrieval. IEEE/ACM Trans Audio Speech Lang Process 24(4):694–707

    Article  Google Scholar 

  • Pang L, Lan Y, Guo J, Xu J, Wan S, Cheng X (2016) Text matching as image recognition. AAAI Press, AAAI’16

  • Rabelo J, Kim MY, Goebel R (2019a) Combining similarity and transformer methods for case law entailment. In: Proceedings of the seventeenth international conference on artificial intelligence and law, pp 290–296

  • Rabelo J, Kim MY, Goebel R (2019b) Combining similarity and transformer methods for case law entailment. In: Proceedings of the seventeenth international conference on artificial intelligence and law, association for computing machinery, New York, NY, USA, ICAIL ’19, pp 290–296

  • Robertson SE, Walker S (1994) Some simple effective approximations to the 2-poisson model for probabilistic weighted retrieval. In: SIGIR’94. Springer, pp 232–241

  • Salakhutdinov R, Hinton G (2009) Semantic hashing. Int J Approx Reason 50(7):969–978

    Article  Google Scholar 

  • Salton G, Buckley C (1988) Term-weighting approaches in automatic text retrieval. Inf Process Manag 24(5):513–523

    Article  Google Scholar 

  • Saracevic T (1996) Relevance reconsidered. In: Proceedings of the second conference on conceptions of library and information science (CoLIS 2), ACM New York, pp 201–218

  • Saravanan M, Ravindran B, Raman S (2009) Improving legal information retrieval using an ontological framework. Artif Intell Law 17(2):101–124

    Article  Google Scholar 

  • Shao Y, Mao J, Liu Y, Ma W, Satoh K, Zhang M, Ma S (2020) Bert-pli: modeling paragraph-level interactions for legal case retrieval. In: Bessiere C (ed) Proceedings of the twenty-ninth international joint conference on artificial intelligence, IJCAI-20, international joint conferences on artificial intelligence organization, pp 3501–3507

  • Shen Y, He X, Gao J, Deng L, Mesnil G (2014) A latent semantic model with convolutional-pooling structure for information retrieval. In: Proceedings of the 23rd ACM international conference on conference on information and knowledge management, pp 101–110

  • Song F, Croft WB (1999) A general language model for information retrieval. In: Proceedings of the eighth international conference on Information and knowledge management, pp 316–321

  • Tai KS, Socher R, Manning CD (2015) Improved semantic representations from tree-structured long short-term memory networks. In: Proceedings 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), Association for Computational Linguistics, Beijing, China, pp 1556–1566

  • Tran V, Le Nguyen M, Tojo S, Satoh K (2020) Encoded summarization: summarizing documents into continuous vector space for legal case retrieval. Artif Intell Law 1–27

  • Van Opijnen M, Santos C (2017) On the concept of relevance in legal information retrieval. Artif Intell Law 25(1):65–87

    Article  Google Scholar 

  • Wan S, Lan Y, Guo J, Xu J, Pang L, Cheng X (2016) A deep architecture for semantic matching with multiple positional sentence representations. In: Proceedings of the thirtieth AAAI conference on artificial intelligence. AAAI Press, AAAI’16, pp 2835–2841

  • Wu Q, Burges CJ, Svore KM, Gao J (2010) Adapting boosting for information retrieval measures. Inf Retr 13(3):254–270

    Article  Google Scholar 

  • Yilmaz ZA, Yang W, Zhang H, Lin J (2019) Cross-domain modeling of sentence-level evidence for document retrieval. In: Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP), pp 3481–3487

  • Zeng Y, Wang R, Zeleznikow J, Kemp E (2005) Knowledge representation for the intelligent legal case retrieval. In: International conference on knowledge-based and intelligent information and engineering systems. Springer, pp 339–345

  • Zhai C, Lafferty J (2017) A study of smoothing methods for language models applied to ad hoc information retrieval, vol 51. ACM SIGIR Forum, ACM New York, NY, USA, pp 268–276

Download references

Acknowledgements

This work was supported by JSPS Kakenhi Grant nos. 20H04295, 20K20406, and 20K20625. The research also was supported in part by the Asian Office of Aerospace R &D (AOARD), Air Force Office of Scientific Research (Grant no. FA2386-19-1-4041).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Le-Minh Nguyen.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendices

Appendix 1: Legal case retrieval on a large number of “noticed” case sample

See Table 13.

Table 13 Sample 522 in COLIEE Task 1 2020

Appendix 2: Legal case retrieval on limited “noticed” cases sample

See Table 14.

Table 14 Sample 521 in COLIEE Task 1 2020

Appendix 3: Sample in legal case entailment phase

See Table 15.

Table 15 Sample 414 in COLIEE Task 2 2020 (P: Prediction, G: Gold label)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Vuong, Y.TH., Bui, Q.M., Nguyen, HT. et al. SM-BERT-CR: a deep learning approach for case law retrieval with supporting model. Artif Intell Law 31, 601–628 (2023). https://doi.org/10.1007/s10506-022-09319-6

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10506-022-09319-6

Keywords

Navigation