=Paper=
{{Paper
|id=Vol-2482/paper29
|storemode=property
|title=International Workshop on Legal Data Analytics and Mining
(LeDAM 2018): Preface to the Proceedings
|pdfUrl=https://ceur-ws.org/Vol-2482/paper29.pdf
|volume=Vol-2482
|authors=Arindam Pal,Arnab Bhattacharya,Indrajit Bhattacharya,Kripabandhu Ghosh,Lipika Dey,Marie-Francine Moens,Saptarshi Ghosh
|dblpUrl=https://dblp.org/rec/conf/cikm/00010B0DMG18
}}
==International Workshop on Legal Data Analytics and Mining
(LeDAM 2018): Preface to the Proceedings==
https://ceur-ws.org/Vol-2482/paper29.pdf
International Workshop on Legal Data Analytics and Mining
(LeDAM 2018): Preface to the Proceedings
Arindam Pal∗ Arnab Bhattacharya§ Indrajit Bhattacharya∗
Kripabandhu Ghosh§ Lipika Dey∗ Marie-Francine Moens†
Saptarshi Ghosh‡
Tata Consultancy Services Research, India∗
Indian Institute of Technology Kanpur, India§
Katholieke Universiteit Leuven, Belgium†
Indian Institute of Technology Kharagpur, India‡
1 INTRODUCTION 2 DETAILS OF INVITED TALKS
Legal data mining is the subarea of data mining applied to legal texts, The LeDAM 2018 workshop included the following invited talks.
such as legislation, case law, patents, and scholarly works. Legal
data mining systems are important to provide easier access to and • Speaker: Giovanni Sartor, Professor of Legal Informatics
insights about law for both common persons and legal profession- and Legal Theory, European University Institute, Italy
als. This area is becoming increasingly important, because of the Title: Using Machine Learning to Support Law Enforce-
rapidly growing volume of legal cases and documents available in ment to the Benefit of Consumers and Data Subject: the
digital formats. For this reason, we organized the First International CLAUDETTE Project
Workshop on Legal Data Analytics and Mining (LeDAM 2018), Abstract: The project CLAUDETTE aims to support the de-
co-located with ACM CIKM 2018. The website of LeDAM 2018 is tection of potentially unfair and unlawful clause, both in con-
https://sites.google.com/site/legaldam2018/. The objectives of the sumer contacts and in privacy policies, through automated
LeDAM 2018 workshop are to: (1) Provide a venue for academic tools, based on computational linguistic and artificial intelli-
and industrial/governmental researchers and professionals to come gence. The purpose is to enable consumer protection bodies
together, present and discuss research results, use cases, innovative and data protection authorities to engage more proactively
ideas, challenges, and opportunities that arise from applications and effectively in monitoring compliance and in enforcing
of data mining in the legal domain, and (2) Foster collaborations the law. With regard to both contract terms and privacy pol-
between the Legal and the Artificial Intelligence, Data Mining, In- icy we have collected a corpus of contract terms, identified
formation Retrieval, and Machine Learning communities. different kinds of unlawful and unfair terms through legal
The workshop programme included invited talks by the follow- analysis, and annotated the documents accordingly. Then we
ing reputed researchers (see Section 2 for details): have applied and tested different computational approaches,
• Giovanni Sartor, Professor of Legal Informatics and Legal including various machine learning algorithms, to detect
Theory, European University Institute, Italy such terms. The better performing algorithms have been im-
• Luigi Di Caro, Assistant Professor, Department of Computer plemented in an application available to the public through
Science, University of Turin, Italy the project’s web site. The system is complemented by a
• Jack G. Conrad, Lead Research Scientist, Center for AI and crawler, that detects changes in the contract and policies
Cognitive Computing, Thomson Reuters Labs, USA already submitted to the system.
The program also included presentation of papers accepted through • Speaker: Luigi Di Caro, Assistant Professor, Department of
the peer-reviewed track (see Section 3), and a panel discussion on Computer Science, University of Turin, Italy
emerging problems in legal data mining. We specifically attempted Title: Natural Language Processing and Ontology Learning
to ensure the presence of both academicians from the data min- in the Legal Domain
ing/IR/ML communities as well as practitioners from the Law in- Abstract: Legal ontologies aim to provide a structured repre-
dustry among our invited speakers and members of our Program sentation of legal concepts and their interconnections. These
Committee (stated in Section 3). For further details, refer to the ontologies are then exploited to support tasks such as in-
LeDAM 2018 website https://sites.google.com/site/legaldam2018/. formation extraction and question answering in the legal
domain. Given the increasing importance of the Web of Data
in public administration and in companies, being able to
Copyright © CIKM 2018 for the individual papers by the papers' provide machine-readable legal information is becoming a
authors. Copyright © CIKM 2018 for the volume as a collection
by its editors. This volume and its papers are published under
the Creative Commons License Attribution 4.0 International (CC
BY 4.0).
valuable and desired contribution. However, concepts and re- • Karl Branting, MITRE Corporation, USA
lations within existing ontologies usually represent limited • Katie Atkinson, University of Liverpool, UK
subjective and application-oriented views of specific sub- • Ken Satoh, National Institute of Informatics, Japan
domains of interest. The talk will discuss resent research on • Kevin Ashley, University of Pittsburgh, USA
natural language technologies and text mining approaches • Matthias Grabmair, Carnegie Mellon University, USA
towards the creation, the reuse and the enrichment of legal • Maura Grossman, University of Waterloo, Canada
ontologies. • Mi-Young Kim, University of Alberta, Canada
• Mossab Bagdouri, Walmart Labs, USA
• Speaker: Jack G. Conrad, Lead Research Scientist, Center • Paulo Quaresma, Universidade de Evora, Portugal
for AI and Cognitive Computing, Thomson Reuters, USA • Prasenjit Majumder, DAIICT, India
Title: 30 Years of AI and Law: Legal Data Analytics in the • William Webber, William Webber Consulting, Australia
Long View – Looking Back, Looking Forward Five papers were accepted through the peer-review process. The
Abstract: This talk will begin by examining the roots of Arti- papers were on various topics, including contract renewals, con-
ficial Intelligence and Law – including applications involving cept hierarchy extraction, patent clustering, argumentation-driven
NLP, data mining, machine learning, and more broadly, data information extraction, deep ensemble learning. The list of papers
analytics – noting that it has been around for much longer accepted in LeDAM 2018 is as follows.
than the recent buzz would suggest. We will explore the field • Title: Structural Analysis of Contract Renewals
of AI and Law in terms of its development and expansion Authors: Frieda Josi and Christian Wartena
starting in the 1980s and study how seminal research was
conducted and reported on in conference proceedings such • Title: Concept Hierarchy Extraction from Legal Literature
as ICAIL and publications such as the AI and Law journal. Authors: Sabine Wehnert, David Broneske, Stefan Langer
After having established the foundations of today’s field of and Gunter Saake
AI and Law, we will look to the future and sketch some of
the practical application scenarios that the capabilities from • Title: Use of Pseudo Relevance Feedback for Patent Cluster-
the field promise to deliver. These include next-generation ing with Fuzzy C-means
tools for legal professionals that can augment their skill sets Authors: Noushin Fadaei and Thomas Mandl
by providing analytical abilities to help in the crafting of
legal strategies. We will illustrate such instruments through • Title: Argumentation-driven information extraction for on-
the visualization of expected outcomes, while varying key line crime reports
parameters such as trial length, expected costs, and likely Authors: Marijn Schraagen, Bas Testerink, Daphne Odek-
award or settlement figures. Lastly, we will investigate the erken and Floris Bex
prospective role that prediction tools can play in AI and Law
application spaces, while looking still further into the future. • Title: Deep Ensemble Learning for Legal Query Understand-
ing
3 PEER-REVIEWED PAPER TRACK Authors: Arunprasath Shankar and Venkata Nagaraju Bud-
Eight papers were submitted to the peer-review track, from diverse darapu
countries all over the world. Each submitted paper was reviewed
by at least three members of the following Program Committee:
• Adam Wyner, Swansea University, Swansea, UK 4 ACKNOWLEDGEMENTS
• Charles K. Nicholas, University of Maryland Baltimore County, We are grateful to the CIKM 2018 workshop chairs Francesco Bonchi
USA and Dimitris Gunopulos for their help and support. We are thankful
• Dave Lewis, Brainspace - A Cyxtera Business, USA to all the authors for submitting their papers to our workshop. We
• Girish Keshav Palshikar, Tata Consultancy Services, India thank the PC members for carefully reviewing the papers. Last,
• Haozhen Zhao, Legal Technology Solution Practice, Navi- but not the least, we are grateful to Paheli Bhattacharya for being
gant the web chair (along with Kripabandhu Ghosh) and keeping the
• Jack G. Conrad, Thomson Reuters, USA website running and up-to-date.
• Jeroen Keppens, King’s College London, UK