An expert system for drafting legal documents

Sprowl, James A.; Balasubramanian, Periyasamy; Chinwalla, Taizoon; Evens, Martha; Klawans, Henriette

doi:10.1145/1499310.1499396

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…Machine learning methods have been applied in the past to various problems in the legal domain [8]- [10], such as outcome forecasting [11]- [15], document discovery [16], [17], document categorization [18]- [24] and legal drafting [25]- [27].…”

Section: Related Workmentioning

confidence: 99%

Determining Standard Occupational Classification Codes from Job Descriptions in Immigration Petitions

Merz¹,

Widmark²,

DiMascio³

et al. 2021

Preprint

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Accurate specification of standard occupational classification (SOC) code is critical to the success of many U.S. work visa applications. Determination of correct SOC code relies on careful study of job requirements and comparison to definitions given by the U.S. Bureau of Labor Statistics, which is often a tedious activity. In this paper, we apply methods from natural language processing (NLP) to computationally determine SOC code based on job description. We implement and empirically evaluate a broad variety of predictive models with respect to quality of prediction and training time, and identify models best suited for this task.

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Section: Related Workmentioning

confidence: 99%

Determining Standard Occupational Classification Codes from Job Descriptions in Immigration Petitions

Merz¹,

Widmark²,

DiMascio³

et al. 2021

Preprint

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“…For example, contracts between service providers and customers/clients often tend to include similar clauses. Unsurprisingly, automated legal drafting, also known as automated document assembly, has been an active area of research for at least the past three decades [23]. As of this writing, machine learning based approaches to drafting are viewed as promising [24], [25].…”

Section: Legal Draftingmentioning

confidence: 99%

Immigration Document Classification and Automated Response Generation

Mukherjee

Oates²,

DiMascio

et al. 2020

2020 International Conference on Data Mining Workshops (ICDMW)

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In this paper, we consider the problem of organizing supporting documents vital to U.S. work visa petitions, as well as responding to Requests For Evidence (RFE) issued by the U.S. Citizenship and Immigration Services (USCIS). Typically, both processes require a significant amount of repetitive manual effort. To reduce the burden of mechanical work, we apply machine learning methods to automate these processes, with humans in the loop to review and edit output for submission. In particular, we use an ensemble of image and text classifiers to categorize supporting documents. We also use a text classifier to automatically identify the types of evidence being requested in an RFE, and used the identified types in conjunction with response templates and extracted fields to assemble draft responses. Empirical results suggest that our approach achieves considerable accuracy while significantly reducing processing time.

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