Purposes and challenges of legal network analysis on case law

Legal Network Analysis (LNA) studies predominantly focus on citation analysis. LNA has served various purposes, including determining the relevance of court decisions in terms of them being precedents, to explore how the relevance changed over time, and to examine which cases are similar based on their proximity in the citation network (community detection). LNA researchers have relied on various network analysis measures when answering their research questions. This raises the question which approaches can or should be used in order for LNA to produce meaningful results. Focusing on case law, this contribution discusses the purposes and challenges of LNA. More specifically, it will be shown that LNA lacks a proper reference point for evaluating the results and that, as a result, a methodology needs to be developed in order to produce results that are valid. Four specific aspects are subsequently explored more in-depth: (1) how to select sub-networks, (2) which community detection method to select, (3) estimating the probability that the network and its relationships as observed in the data did not occur by chance, and (4) which centrality measure to select to determine the extent to which a decision is a precedent. By examining these purposes and challenges, we aim to develop a research agenda for conducting LNA. Possible avenues for future research are discussed

Segmenting accelerometer data from daily life with unsupervised machine learning

Purpose: Accelerometers are increasingly used to obtain valuable descriptors of physical activity for health research. The cut-points approach to segment accelerometer data is widely used in physical activity research but requires resource expensive calibration studies and does not make it easy to explore the information that can be gained for a variety of raw data metrics. To address these limitations, we present a data-driven approach for segmenting and clustering the accelerometer data using unsupervised machine learning. Methods: The data used came from five hundred fourteen-year-old participants from the Millennium cohort study who wore an accelerometer (GENEActiv) on their wrist on one weekday and one weekend day. A Hidden Semi-Markov Model (HSMM), configured to identify a maximum of ten behavioral states from five second averaged acceleration with and without addition of x, y, and z-angles, was used for segmenting and clustering of the data. A cut-points approach was used as comparison. Results: Time spent in behavioral states with or without angle metrics constituted eight and five principal components to reach 95% explained variance, respectively; in comparison four components were identified with the cut-points approach. In the HSMM with acceleration and angle as input, the distributions for acceleration in the states showed similar groupings as the cut-points categories, while more variety was seen in the distribution of angles. Conclusion: Our unsupervised classification approach learns a construct of human behavior based on the data it observes, without the need for resource expensive calibration studies, has the ability to combine multiple data metrics, and offers a higher dimensional description of physical behavior. States are interpretable from the distributions of observations and by their duration

Screenshot case-law-app

This is a screenshot of the Case Law network visualization application (https://nlesc.github.io/case-law-app/), to be used in presentations or articles

stroll-srl

Code for stroll SRL model in Dutch

e2e-Dutch

Code for e2e coref model in Dutch. The code is based on the original e2e model for English (https://github.com/kentonl/e2e-coref), and modified to work for Dutch

NLeSC/SalientDetector-matlab: Release code AICCSA 2016

MATLAB Software for Large Scale Imaging research @ NLeS

Netwerkanalyse van werkgeversaansprakelijkheid: Deelonderwerpen en precedenten in het kader van werkgeversaansprakelijkheid ex artikel 7:658/7:611 BW

In dit artikel wordt voor het thema werkgeversaansprakelijkheid ex artikel 7:658 BW en artikel 7:611 BW getracht te achterhalen welke deelonderwerpen te onderscheiden zijn en wat de precedenten zijn binnen de verschillende deelonderwerpen. Het artikel is het eerste dat voor een privaatrechtelijk thema deelonderwerpen en precedenten opspoort aan de hand van een netwerkanalyse. Netwerkanalyse is een handig hulpmiddel om de problematiek rond, in dit geval, werkgeversaansprakelijkheid sneller en beter te begrijpen, dat tijd kan besparen, andere inzichten kan opleveren en nieuwe vragen kan opwerpen

e2e-Dutch

Code for e2e coref model in Dutch. The code is based on the original e2e model for English (https://github.com/kentonl/e2e-coref), and modified to work for Dutch