1,720,966 research outputs found
Towards visualizing and analysing legal proceedings with process mining
Full text linkProcess mining (PM) is a family of data-driven techniques which use data with the goal of studying the process behind the data, i.e., the data-generating process. Despite initially tailored for the engineering and industrial domain, it is becoming popular also in more human-centric domains like the legal and healthcare ones. This paper proposes preliminary steps towards a general-purpose process mining methodology utilizing Fluxicon’s Disco tool aimed at analyzing and optimizing the complex processes underlying legal decision making by Courts. We consider specifically the domain of civil proceedings, with a focus on divorce cases. In PM terms, a case is a legal proceeding, and activities are the different internal phases in which a legal case transits from its beginning to the final judgment. The studied process is, therefore, the internal process followed by the Court, possibly varying over the years, to handle specific types of proceedings. By leveraging process mining techniques, this preliminary study examines the evolution of divorce proceedings within a selected Italian court in the time frame 2013-2019, identifying key performance indicators and uncovering hidden process inefficiencies and efficiencies. The findings highlight the potential of process mining to reveal critical process patterns, enabling organizations to make data-driven decisions and implement targeted process improvements
Automatic Rhetorical Roles Classification for Legal Documents using LEGAL-TransformerOverBERT
Full text linkAutomatic identification of rhetorical roles can help in many downstream applications of legal documents analysis, such as legal decisions summarization and legal search. This is usually a complex task, even for humans, due to its inherent subjectivity and to the difficulty of capturing sentence context in very long legal documents. We propose a novel approach, based on Hierarchical Transformers, which overcomes these problems and achieves promising results on two different datasets of Italian and English legal judgments. Specifically, we introduce LEGAL-TransformerOverBERT (LEGAL-ToBERT), a model based on the stacking of a transformer encoder over a legal-domain-specific BERT model, and show that our approach is able to significantly improve the baselines set by the stand-alone LEGAL-BERT models, by capturing the relationships between different sentences of the same document. We make our models available and ready-to-use for downstream applications of rhetorical roles classification in the legal context both for the Italian and English language
Automatic Anonymization of Italian Legal Textual Documents using Deep Learning
No full textThe dissemination of judicial decisions not only provides a valuable source of decision support for judges and legal practitioners but also strengthens public confidence in the judicial system. However, the nature of the data raises privacy concerns as the documents include personal and, often, sensitive data such as health, financial, religious beliefs, sexual orientation, and so on. In recent years, especially since the introduction of GDPR, the international scientific community has paid much attention to the issue of privacy and automatic anonymization tools, but unfortunately, nothing has been done in the Italian legal context.
In this paper, we present a first solution aimed at automatic anonymization of the Italian National Jurisprudential Archive (Archivio Giurisprudenziale Nazionale) domain based on pre-trained Transformers embeddings (Clark et al., 2020, Devlin et al., 2019) and spaCy’s transition-based parsing for entity recognition (Honnibal and Montani, 2017). It achieves more than 94.7% recall (>99% for Person and ID entities) and supports several anonymization methods that can be applied to the text depending on the purpose of anonymizatio
Towards Novel Statistical Methods for Anomaly Detection in Industrial Processes
Full text linkThis paper presents a novel methodology based on first principles of statistics and statistical learning for anomaly detection in industrial processes and IoT environments. We present a 5-level analytical pipeline that cleans, smooths, and eliminates redundancies from the data, and identifies outliers as well as the features that contribute most to these anomalies. We show how smoothing can make our methodology less sensitive to short-lived anomalies that might be, e.g., due to sensor noise. We validate the methodology on a dataset freely available in the literature. Our results show that we can identify all anomalies in the considered dataset, with the ability of controlling the amount of false positives. This work is the result of a research project co-funded by the Tuscany Region and a company leader in the paper and nonwovens sector. Although the methodology was developed for this domain, we consider here a dataset from a different industrial sector. This shows that our methodology can be generalized to other contexts with similar constraints on limited resources, interpretability, time, and budget
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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