Data-centric and logic-based models for automated legal problem solving

Branting, L. Karl

doi:10.1007/s10506-017-9193-x

Cited by 49 publications

(31 citation statements)

References 35 publications

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“…Of course, a real contract lawyer may often perform this job better, for example by ascertaining -through additional questioning or other 10 Tyree (1989). 11 Branting (2017) uses the term logic-based, instead of rule-based, to distinguish from systems using data science tecniques to assist in solving practical legal problems. For example, "robot-lawyer" ROSS, based on IBM's Watson, could be considered a system that is primarily data-centric (or: case-based), although it will, no doubt, use a variety of techniques.…”

Section: Terminologymentioning

confidence: 99%

Contract Automation: Experiences from Dutch Legal Practice

Timmer

2019

Legal Tech, Smart Contracts and Blockchain

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Organizations in legal practice, under pressure to do "more for less," are searching for ways to automate legal work, to improve efficiency of legal service delivery. Automated drafting of contracts (or: contract automation) is one of the areas where technology is-partly-replacing legal professionals. In Dutch legal practice, the number of organizations that are actively deploying contract automation is still relatively small, but growing. This chapter looks at experiences with contract automation of organizations from various sectors in Dutch legal practice. Contract automation can improve legal service delivery to consumers and SMEs, as well as contracting processes within organizations. Several organizations report positive results. However, successfully implementing contract automation, especially for internal use within organizations, is not simple. Tight budgets, resistance to change and poor integration with other software are some of the problems that organizations may encounter. Generally, human and organizational factors are often at least as important as the technological aspects. Succesful implementation of contract automation requires design thinking, a proactive approach and process-oriented (legal) professionals. Regardless of these difficulties, the use of contract automation software in Dutch legal practice can be expected to increase, due to several factors. The number of organizations that are offering contracts (and other legal documents) online to SMEs and consumers has grown rapidly over the last years. Contract automation is not only offered to consumers and SMEs by commercial parties, but also by branch organizations, as a service to their members. Consumers and SMEs will become used to these self-help solutions for legal matters. Legal publishers are also increasing the offering of automated contracts and other legal documents. In addition, law firms and consultants are promoting the use of contract automation within client organizations. Finally, many corporate organizations are increasingly exchanging experiences on improving legal operations and the use of Legal Tech, including contract automation. Eventually, increased use of contract automation may drive further harmonization of contracts within sectors and facilitate other technological applications, such as the automated analysis of contracts.

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Section: Terminologymentioning

confidence: 99%

Contract Automation: Experiences from Dutch Legal Practice

Timmer

2019

Legal Tech, Smart Contracts and Blockchain

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“…Note that the idea is to identify usable arguments, not to replicate the argumentation process. As Branting remarks in [20], "[machine learning] techniques typically are opaque and unable to support the rule-governed discourse needed for persuasive argumentation and justification".…”

Section: Al-abdulkarim Et Al / Building An Application Using the mentioning

confidence: 99%

“…21 19 Microsoft 'deeply sorry' for racist and sexist tweets by AI chatbot, https://www.theguardian.com/technology/2016/mar/26/ microsoft-deeply-sorry-for-offensive-tweets-by-ai-chatbot. 20 https://www.theguardian.com/law/2011/apr/11/judges-lenient-break. 21 This study used features based on the Supreme Court Database (Spaeth HJ, Epstein L, Martin AD, Segal JA, Ruger TJ, Benesh SC.…”

Section: Problems With Uncritical Use Of Past Decisionsmentioning

confidence: 99%

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Noise induced hearing loss: Building an application using the ANGELIC methodology

Al-Abdulkarim

Atkinson

Bench‐Capon

et al. 2018

AAC

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The ANGELIC methodology was developed to encapsulate knowledge of particular legal domains. In this paper we describe its use to build a full scale practical application intended to be used internally by Weightmans, a large firm of legal practitioners with branches throughout the UK. We describe the domain, Noise Induced Hearing Loss (NIHL), the intended task, the sources used, the stages in development and the resulting application. An assessment of the project and its potential for further development is discussed. The project shows that current academic research using computational models of argument can prove useful to legal practitioners confronted by real legal tasks.

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“…Moreover, semantic relationships among the case judgments and their interpretation are implicitly available as text within a judgment document. Natural language processing (NLP), along with machine learning methods are used to establish semantic relevance of textual contents present in the documents (Branting, 2017). Until recently, Vector Space Model and Latent Semantic Indexing, LSI with its variants were used largely for semantic representation of text.…”

Section: Introductionmentioning

confidence: 99%

Legal document similarity: a multi-criteria decision-making perspective

Wagh

Anand

2020

PeerJ Computer Science

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The vast volume of documents available in legal databases demands effective information retrieval approaches which take into consideration the intricacies of the legal domain. Relevant document retrieval is the backbone of the legal domain. The concept of relevance in the legal domain is very complex and multi-faceted. In this work, we propose a novel approach of concept based similarity estimation among court judgments. We use a graph-based method, to identify prominent concepts present in a judgment and extract sentences representative of these concepts. The sentences and concepts so mined are used to express/visualize likeness among concepts between a pair of documents from different perspectives. We also propose to aggregate the different levels of matching so obtained into one measure quantifying the level of similarity between a judgment pair. We employ the ordered weighted average (OWA) family of aggregation operators for obtaining the similarity value. The experimental results suggest that the proposed approach of concept based similarity is effective in the extraction of relevant legal documents and performs better than other competing techniques. Additionally, the proposed two-level abstraction of similarity enables informative visualization for deeper insights into case relevance.

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Data-centric and logic-based models for automated legal problem solving

Cited by 49 publications

References 35 publications

Contract Automation: Experiences from Dutch Legal Practice

Contract Automation: Experiences from Dutch Legal Practice

Noise induced hearing loss: Building an application using the ANGELIC methodology

Legal document similarity: a multi-criteria decision-making perspective

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