Legal Information Retrieval (IR) Task
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A Legal Information Retrieval (IR) Task is an domain-specific IR task focused on retrieving legal documents from a corpus that are relevant to a legal search query.
- Context:
- Input: Legal Search Query; Corpus (e.g. a legal corpus).
- measure: Legal Document Retrieval Accuracy, likely is evaluated using legal experts' assessments of relevance.
- It can be solved by a Legal IR System (that implements a legal IR algorithm).
- ...
- Examples:
- Legal Cases Retrieval, of relevant legal cases (given facts, claims, and legal questions related to a new case).
- Legal Statutes Retrieval, of relevant statutes and regulations (for a legal question).
- Patent Prior Art Retrieval, of relevant prior art documents (for a new patent application).
- Contract Clauses Retrieval, of relevant contract clauses to drafting a new contract.
- Related Precedents Retrieval, of key legal precedents related to a legal case from a case law database.
- SLA Documents Retrieval, of SLA documents relevant to drafting a new SLA agreement.
- Commercial Contracts Retrieval, of relevant commercial contracts relevant to drafting a new contract.
- Terms and Conditions Retrieval, of terms and conditions relevant to formulating new T&Cs.
- ...
- Counter-Examples:
- General Web Search Task - not specialized to legal domain.
- Legal Document Classification - predicts categories rather than retrieves documents.
- Legal Text Generation - generates text rather than searches documents.
- ...
- See: Legal Document Similarity, Legal Corpus, Legal KB, Legal AI.
References
2023
- (Wikipedia, 2023) ⇒ https://en.wikipedia.org/wiki/Legal_information_retrieval Retrieved:2023-8-23.
- Legal information retrieval is the science of information retrieval applied to legal text, including legislation, case law, and scholarly works. [1] Accurate legal information retrieval is important to provide access to the law to laymen and legal professionals. Its importance has increased because of the vast and quickly increasing amount of legal documents available through electronic means.[2] Legal information retrieval is a part of the growing field of legal informatics. In a legal setting, it is frequently important to retrieve all information related to a specific query. However, commonly used boolean search methods (exact matches of specified terms) on full text legal documents have been shown to have an average recall rate as low as 20 percent,[3] meaning that only 1 in 5 relevant documents are actually retrieved. In that case, researchers believed that they had retrieved over 75% of relevant documents.[3] This may result in failing to retrieve important or precedential cases. In some jurisdictions this may be especially problematic, as legal professionals are ethically obligated to be reasonably informed as to relevant legal documents. [4] Legal Information Retrieval attempts to increase the effectiveness of legal searches by increasing the number of relevant documents (providing a high recall rate) and reducing the number of irrelevant documents (a high precision rate). This is a difficult task, as the legal field is prone to jargon, [5] polysemes [6] (words that have different meanings when used in a legal context), and constant change.
Techniques used to achieve these goals generally fall into three categories: boolean retrieval, manual classification of legal text, and natural language processing of legal text.
- Legal information retrieval is the science of information retrieval applied to legal text, including legislation, case law, and scholarly works. [1] Accurate legal information retrieval is important to provide access to the law to laymen and legal professionals. Its importance has increased because of the vast and quickly increasing amount of legal documents available through electronic means.[2] Legal information retrieval is a part of the growing field of legal informatics. In a legal setting, it is frequently important to retrieve all information related to a specific query. However, commonly used boolean search methods (exact matches of specified terms) on full text legal documents have been shown to have an average recall rate as low as 20 percent,[3] meaning that only 1 in 5 relevant documents are actually retrieved. In that case, researchers believed that they had retrieved over 75% of relevant documents.[3] This may result in failing to retrieve important or precedential cases. In some jurisdictions this may be especially problematic, as legal professionals are ethically obligated to be reasonably informed as to relevant legal documents. [4] Legal Information Retrieval attempts to increase the effectiveness of legal searches by increasing the number of relevant documents (providing a high recall rate) and reducing the number of irrelevant documents (a high precision rate). This is a difficult task, as the legal field is prone to jargon, [5] polysemes [6] (words that have different meanings when used in a legal context), and constant change.
2023
- "Legal IR and NLP: the History, Challenges, and State-of-the-Art." Tutorial at ECIR-2023.
- SUMMARY: Artificial Intelligence (AI), Machine Learning (ML), Information Retrieval (IR) and Natural Language Processing (NLP) are transforming the way legal professionals and law firms approach their work. The significant potential for the application of AI to Law, for instance, by creating computational solutions for legal tasks, has intrigued researchers for decades. This appeal has only been amplified with the advent of Deep Learning (DL). It is worth noting that working with legal text is far more challenging than in many other subdomains of IR/NLP, mainly due to factors like lengthy documents, complex language and lack of large-scale datasets. In this tutorial, we shall introduce the audience to the nature of legal systems and texts, and the challenges associated with processing legal documents.
- 4 State-of-the-art survey Debasis Ganguly, Paheli Bhattacharya and Kripabandhu Ghosh Slides
- 5 Industry perspective Jack G. Conrad Slides
- 6 Future directions, advent of LLMs and explainability Jack G. Conrad, Kripabandhu Ghosh and Saptarshi Ghosh Slides
- 7 Hands-on coding Debasis Ganguly, Paheli Bhattacharya, Shounak Paul and Shubham Kumar Nigam JuPyter Notebook
- ↑ Maxwell, K.T., and Schafer, B. 2009, p. 1
- ↑ Jackson et al., p. 60
- ↑ 3.0 3.1 Blair, D.C., and Maron, M.E., 1985, p.293
- ↑ American Bar Association, Model Rules of Professional Conduct Rule 1.1, http://www.abanet.org/cpr/mrpc/rule_1_1.html
- ↑ Peters, W. et al. 2007, p. 118
- ↑ Peters, W. et al. 2007, p. 130