The International Workshop on Mining and Learning in the Legal Domain (MLLD 2021)https://sites.google.com/view/mlld2021/home will be co-located with ICDM-2021https://icdm2021.auckland.ac.nz/ to be held on December 7-10, 2021, in Auckland, New Zealand.
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
Following the success of the 1st MLLD workshop (MLLD 2020)https://sites.google.com/view/mlld2020/home, the 2nd workshop on Mining and Learning in the Legal Domain (MLLD 2021) discusses a broad variety of topics in various aspects of analyzing legal data such as Legislations, litigations, court cases, contracts, patents, Non-Disclosure Agreements (NDAs) and Bylaws. We encourage submissions on novel mining and learning based solutions in:
* 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 CyberChairhttps://www.wi-lab.com/cyberchair/2021/icdm21/. More information about the workshop is available herehttps://sites.google.com/view/mlld2021/home.
Important Dates
* Paper submission due date: September 3, 2021
* Notification of acceptance: September 24, 2021
* Camera ready submission: October 1, 2021
* MLLD -2021 Workshop: December 7, 2021
Organizing Committee
Masoud Makrehchimailto:masoud.makrehchi@uoit.ca, OntarioTech University and Thomson Reuters Labs
Shohreh Shaghaghianmailto:shohreh.shaghaghian@thomsonreuters.com, Thomson Reuters Labs
Ali Vahdatmailto:ali.vahdat@thomsonreuters.com, Thomson Reuters Labs
Fattane Zarrinkalammailto:fattane.zarrinkalam@thomsonreuters.com, Thomson Reuters Labs
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