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The International Workshop on Mining and Learning in the Legal
Domain (MLLD
2020)
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will be co-located with
ICDM-2020
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to be held on November 17-20, 2020, Sorrento, Italy.
Call For Papers
Introduction
The increasing accessibility of large legal corpora and databases
create opportunities to develop data driven techniques as well as
more advanced tools that can facilitate multiple tasks of
researchers and practitioners in the legal domain. While recent
advancements in the areas of data mining and machine learning have
gained many applications in domains such as biomedical, healthcare
and finance, there is still a noticeable gap in how much the
state-of-the-art techniques are being incorporated in the legal
domain. Achieving this goal entails building a multi-disciplinary
community that can benefit from the competencies of both law and
computer science experts. The goal of this workshop is to bring
the researchers and practitioners of both disciplines together and
provide an opportunity to share the latest novel research findings
and innovative approaches in employing data analytics and machine
learning in the legal domain.
Topics
We encourage submissions on novel mining and learning based
solutions in various aspects of analyzing legal data such as
Legislations, litigations, court cases, contracts, patents,
Non-Disclosure Agreements (NDAs) and Bylaws. Topics of interest
include, but are not limited to:
* Applications of data mining techniques in the legal domain
* case outcome prediction
* classifying, clustering and identifying anomalies in big corpora
of legal records
* legal analytics
* citation analysis for case law
* eDiscovery
* Applications of natural language processing and machine learning
techniques for legal textual data
* information extraction and entity extraction/resolution for
legal document reviews
* information retrieval and question answering in applications
such as identifying relevant case law
* summarization of legal documents
* legal language modelling and legal document embedding and
representation
* recommender systems for legal applications
* topic modelling in large amounts of legal documents
* harnessing of deep learning approaches
* Ethical issues in mining legal data
* privacy and GDPR in legal analytics
* bias in the applications of data mining
* transparency in legal data mining
* Training data for legal domain
* digital lawyers and legal machines
* smart contracts
* future of law practice in the age of AI
* Emerging topics in the intersection of data mining and law
* acquisition, representation, indexing, storage, and management
of legal data
* automatic annotation and learning with human in the loop
* data augmentation techniques for legal data
* semi-supervised learning, domain adaptation, distant supervision
and transfer learning
Submissions
You are invited to submit your original research and application
papers to the workshop. As per ICDM instructions, papers are
limited to a maximum of 8 pages, and must follow the IEEE ICDM
format requirements. All accepted workshop papers will be
published in the formal proceedings by the IEEE Computer Society
Press. Each paper is reviewed by at least 3 reviewers from the
program committee. Paper review is triple-blind. Manuscripts are
to be submitted through
CyberChair
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More information about the workshop is available
here<
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elinks.protection.outlook.com/?url=https%3A%2F%2Fsites.google.com%2Fview%2Fmlld2020%2Fhome&data=02%7C01%7CShohreh.Shaghaghian%40thomsonreuters.com%7C8a4c6d483f6a4eb409cf08d7ea3938fd%7C62ccb8646a1a4b5d8e1c397dec1a8258%7C0%7C0%7C637235403796141695&sdata=JURcQuBdqGr8rBVG%2FMKsyA5S7WCpTkPFadnO0kSnyJQ%3D&reserved=0>.
Important Dates
* Paper submission due date: August 24, 2020
* Notification of acceptance: September 17, 2020
* Camera ready submission: September 24, 2020
* MLLD -2020 Workshop: November 17, 2020
Organizing Committee
* Shohreh
Shaghaghian
<mailto:shohreh.shaghaghian@thomsonreuters.com>,
Center for AI and Cognitive Computing at Thomson Reuters
* Masoud Makrechi
<mailto:masoud.makrehchi@uoit.ca>,
OntarioTech University and Center for AI and Cognitive Computing
at Thomson Reuters
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