An Intent Taxonomy of Legal Case Retrieval
- URL: http://arxiv.org/abs/2307.13298v1
- Date: Tue, 25 Jul 2023 07:27:32 GMT
- Title: An Intent Taxonomy of Legal Case Retrieval
- Authors: Yunqiu Shao, Haitao Li, Yueyue Wu, Yiqun Liu, Qingyao Ai, Jiaxin Mao,
Yixiao Ma, Shaoping Ma
- Abstract summary: Legal case retrieval is a special Information Retrieval(IR) task focusing on legal case documents.
We present a novel hierarchical intent taxonomy of legal case retrieval.
We reveal significant differences in user behavior and satisfaction under different search intents in legal case retrieval.
- Score: 43.22489520922202
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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