Incorporating Retrieval Information into the Truncation of Ranking Lists for Better Legal Search

Ma, Yixiao; Ai, Qingyao; Wu, Yueyue; Shao, Yunqiu; Liu, Yiqun; Zhang, Min; Ma, Shaoping

doi:10.1145/3477495.3531998

Cited by 11 publications

(5 citation statements)

References 19 publications

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“…The nearest research line is the document list truncation [2,3] that aims to determine the optimal cutoff position for retrieved documents. Document list truncation has been applied in various domains, including legal document retrieval [38,57] and searching [59,63]. The recent methods [3,36,59,63] formulate the truncation problem as a classification task that predicts the optimal position among the candidate cut-off positions.…”

Section: Related Workmentioning

confidence: 99%

Top-Personalized-K Recommendation

Kweon,

Kang,

Jang

et al. 2024

Proceedings of the ACM Web Conference 2024

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The conventional top-K recommendation, which presents the top-K items with the highest ranking scores, is a common practice for generating personalized ranking lists. However, is this fixed-size top-𝐾 recommendation the optimal approach for every user's satisfaction? Not necessarily. We point out that providing fixed-size recommendations without taking into account user utility can be suboptimal, as it may unavoidably include irrelevant items or limit the exposure to relevant ones. To address this issue, we introduce Top-Personalized-𝐾 Recommendation, a new recommendation task aimed at generating a personalized-sized ranking list to maximize individual user satisfaction. As a solution to the proposed task, we develop a model-agnostic framework named PerK. PerK estimates the expected user utility by leveraging calibrated interaction probabilities, subsequently selecting the recommendation size that maximizes this expected utility. Through extensive experiments on real-world datasets, we demonstrate the superiority of PerK in Top-Personalized-𝐾 recommendation task. We expect that Top-Personalized-𝐾 recommendation has the potential to offer enhanced solutions for various real-world recommendation scenarios, based on its great compatibility with existing models.

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Section: Related Workmentioning

confidence: 99%

Top-Personalized-K Recommendation

Kweon,

Kang,

Jang

et al. 2024

Proceedings of the ACM Web Conference 2024

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“…Neural LCR models rely on encoding the case using the language models [10,11,20,24]. With the increasing amount of online legal information and users' legal information needs, many neural LCR models [1,2,6,8,9,16,17,21,29,33,34,[36][37][38]40] are conducted to bridge the information gap by capturing domain-specific and personal needs. Law2Vec [9] is a legal language model that pre-trains on a large legal corpus.…”

Section: Legal Case Retrievalmentioning

confidence: 99%

“…While in civil law system, although the judgement is not necessarily to be based on previously relevant cases, judges and lawyers are still strongly suggested to obtain legal information from these relevant cases 1 . Nowadays, the methods of LCR can be generally divided into two branches, statistical retrieval models [14,27,32] that measure the term frequency similarity between cases and neural LCR models [1,2,6,8,9,16,17,21,29,33,[36][37][38]41] that encode the case into a representation to conduct nearest neighbour search.…”

Section: Introductionmentioning

confidence: 99%

Prompt-Based Effective Input Reformulation for Legal Case Retrieval

Tang,

Qiu,

2023

Lecture Notes in Computer Science

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Legal case retrieval plays an important role for legal practitioners to effectively retrieve relevant cases given a query case. Most existing neural legal case retrieval models directly encode the whole legal text of a case to generate a case representation, which is then utilised to conduct a nearest neighbour search for retrieval. Although these straightforward methods have achieved improvement over conventional statistical methods in retrieval accuracy, two significant challenges are identified in this paper: (1) Legal feature alignment: the usage of the whole case text as the input will generally incorporate redundant and noisy information because, from the legal perspective, the determining factor of relevant cases is the alignment of key legal features instead of whole text matching;(2) Legal context preservation: furthermore, since the existing text encoding models usually have an input length limit shorter than the case, the whole case text needs to be truncated or divided into paragraphs, which leads to the loss of the global context of legal information. In this paper, a novel legal case retrieval framework, PromptCase, is proposed to tackle these challenges. Firstly, legal facts and legal issues are identified and formally defined as the key features facilitating legal case retrieval based on a thorough study of the definition of relevant cases from a legal perspective. Secondly, with the determining legal features, a promptbased encoding scheme is designed to conduct an effective encoding with language models. Extensive zero-shot experiments have been conducted on two benchmark datasets in legal case retrieval, which demonstrate the superior retrieval effectiveness of the proposed PromptCase. The code has been released on https://github.com/yanran-tang/PromptCase.

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“…In judicial practice, the facts and evidence for the same case tend to be submitted in separate files and are not linked with each other, which may cost the judges a lot of time to retrieve relevant evidence to validate the authenticity of each fact. Though tremendous advances have been made in Legal AI, such as Legal Information Extraction (Chen et al 2020;Yao et al 2022), Legal Case Retrieval (Ma et al 2021(Ma et al , 2022 and Legal Judgment Prediction (Zhong et al 2018), little attention has been paid to evidence-related research and most existing works assume the facts determined by the judges, ignoring the expensive cost behind it.…”

Section: Introductionmentioning

confidence: 99%

“…Due to the vocabulary mismatch problem in LER task, we formulate our task in the dense retrieval paradigm (Karpukhin et al 2020;Sciavolino et al 2021;Zhang et al 2022), where the facts and verbal evidence are encoded into dense embeddings by pretrained models, and the retrieval is conducted in the dense representation space.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Legal Evidence Retrieval via Contrastive Learning with Approximate Aggregated Positive

Feng

Zhang

et al. 2023

AAAI

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Verifying the facts alleged by the prosecutors before the trial requires the judges to retrieve evidence within the massive materials accompanied. Existing Legal AI applications often assume the facts are already determined and fail to notice the difficulty of reconstructing them. To build a practical Legal AI application and free the judges from the manually searching work, we introduce the task of Legal Evidence Retrieval, which aims at automatically retrieving the precise fact-related verbal evidence within a single case. We formulate the task in a dense retrieval paradigm, and jointly learn the constrastive representations and alignments between facts and evidence. To get rid of the tedious annotations, we construct an approximated positive vector for a given fact by aggregating a set of evidence from the same case. An entropy-based denoise technique is further applied to mitigate the impact of false positive samples. We train our models on tens of thousands of unlabeled cases and evaluate them on a labeled dataset containing 919 cases and 4,336 queries. Experimental results indicate that our approach is effective and outperforms other state-of-the-art representation and retrieval models. The dataset and code are available at https://github.com/yaof20/LER.

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Incorporating Retrieval Information into the Truncation of Ranking Lists for Better Legal Search

Cited by 11 publications

References 19 publications

Top-Personalized-K Recommendation

Top-Personalized-K Recommendation

Prompt-Based Effective Input Reformulation for Legal Case Retrieval

Unsupervised Legal Evidence Retrieval via Contrastive Learning with Approximate Aggregated Positive

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