# An Intent Taxonomy of Legal Case Retrieval

arXiv: https://arxiv.org/abs/2307.13298 · HTML: https://mlsanity.com/paper/2307.13298/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 72 references that are in the map.
- First posted on arXiv 25 Jul 2023.

## Builds on (most cited first, 12 of 12)

- [SAILER: Structure-aware Pre-trained Language Model for Legal Case Retrieval](https://mlsanity.com/paper/2304.11370/) (2023)
- [CAIL2019-SCM: A Dataset of Similar Case Matching in Legal Domain](https://mlsanity.com/paper/1911.08962/) (2019)
- [Building Legal Case Retrieval Systems with Lexical Matching and Summarization using A Pre-Trained Phrase Scoring Model](https://mlsanity.com/paper/2009.14083/) (2020)
- [Explainable Legal Case Matching via Inverse Optimal Transport-based Rationale Extraction](https://mlsanity.com/paper/2207.04182/) (2022)
- [Towards Better Web Search Performance: Pre-training, Fine-tuning and Learning to Rank](https://mlsanity.com/paper/2303.04710/) (2023)
- [THUIR@COLIEE 2023: Incorporating Structural Knowledge into Pre-trained Language Models for Legal Case Retrieval](https://mlsanity.com/paper/2305.06812/) (2023)
- [THUIR at WSDM Cup 2023 Task 1: Unbiased Learning to Rank](https://mlsanity.com/paper/2304.12650/) (2023)
- [THUIR@COLIEE 2023: More Parameters and Legal Knowledge for Legal Case Entailment](https://mlsanity.com/paper/2305.06817/) (2023)
- [Case law retrieval: problems, methods, challenges and evaluations in the last 20 years](https://mlsanity.com/paper/2202.07209/) (2022)
- [Discovering Explanatory Sentences in Legal Case Decisions Using Pre-trained Language Models](https://mlsanity.com/paper/2112.07165/) (2021)
- [Intent Models for Contextualising and Diversifying Query Suggestions](https://mlsanity.com/paper/1312.1611/) (2013)
- [Why People Search for Images using Web Search Engines](https://mlsanity.com/paper/1711.09559/) (2017)

## Built on by (most cited first, 6 of 6)

- [LeCaRDv2: A Large-Scale Chinese Legal Case Retrieval Dataset](https://mlsanity.com/paper/2310.17609/) (2023)
- [LexEval: A Comprehensive Chinese Legal Benchmark for Evaluating Large Language Models](https://mlsanity.com/paper/2409.20288/) (2024)
- [MUSER: A Multi-View Similar Case Retrieval Dataset](https://mlsanity.com/paper/2310.15602/) (2023)
- [CaseGen: A Benchmark for Multi-Stage Legal Case Documents Generation](https://mlsanity.com/paper/2502.17943/) (2025)
- [Understanding the User: An Intent-Based Ranking Dataset](https://mlsanity.com/paper/2408.17103/) (2024)
- [Generative Chinese Statute Retrieval](https://mlsanity.com/paper/2607.11109/) (2026)

## Abstract (arXiv)

Legal case retrieval is a special Information Retrieval~(IR) task focusing on legal case documents. Depending on the downstream tasks of the retrieved case documents, users' information needs in legal case retrieval could be significantly different from those in Web search and traditional ad-hoc retrieval tasks. While there are several studies that retrieve legal cases based on text similarity, the underlying search intents of legal retrieval users, as shown in this paper, are more complicated than that yet mostly unexplored. To this end, we present a novel hierarchical intent taxonomy of legal case retrieval. It consists of five intent types categorized by three criteria, i.e., search for Particular Case(s), Characterization, Penalty, Procedure, and Interest. The taxonomy was constructed transparently and evaluated extensively through interviews, editorial user studies, and query log analysis. Through a laboratory user study, we reveal significant differences in user behavior and satisfaction under different search intents in legal case retrieval. Furthermore, we apply the proposed taxonomy to various downstream legal retrieval tasks, e.g., result ranking and satisfaction prediction, and demonstrate its effectiveness. Our work provides important insights into the understanding of user intents in legal case retrieval and potentially leads to better retrieval techniques in the legal domain, such as intent-aware ranking strategies and evaluation methodologies.