Options: Use Forum View

Use Monospaced Font
Show Text Part by Default
Show All Mail Headers

Message: [<< First] [< Prev] [Next >] [Last >>]
Topic: [<< First] [< Prev] [Next >] [Last >>]
Author: [<< First] [< Prev] [Next >] [Last >>]

Print Reply
"Dell Zhang Dr." <[log in to unmask]>
Reply To:
Sun, 20 Aug 2023 06:27:37 -0400
text/plain (114 lines)
NOTICE: The paper submission deadline has been extended to 
Friday September 1st, 2023 (AoE) upon the request of authors.

The 3rd International Workshop on Mining and Learning in the Legal Domain (MLLD)
in conjunction with the 32nd ACM International Conference on
Information and Knowledge Management (CIKM-2023)
University of Birmingham and Eastside Rooms, UK
Sunday 22nd October 2023

# Important Dates

- Paper submission deadline: September 1st, 2023
- Paper acceptance notification: September 15th, 2023
- Paper final version due: October 1st, 2023
- Workshop date: October 22nd, 2023

# Abstract

The increasing accessibility of legal corpora and databases create
opportunities to develop data-driven techniques and advanced tools
that can facilitate a variety of tasks in the legal domain, such as
legal search and research, legal document review and summary, legal
contract drafting, and legal outcome prediction. Compared with other
application domains, the legal domain is characterized by the huge
scale of natural language text data, the high complexity of specialist
knowledge, and the critical importance of ethical considerations. The
MLLD workshop aims to bring together researchers and practitioners to
share the latest research findings and innovative approaches in
employing data mining, machine learning, information retrieval, and
knowledge management techniques to transform the legal sector.
Building upon the previous successes, the third edition of the MLLD
workshop will emphasize the exploration of new research opportunities
brought about by recent rapid advances in Large Language Models and
Generative AI. We encourage submissions that intersect computer
science and law, from both academia and industry, embodying the
interdisciplinary spirit of CIKM.

# Topics

We encourage submissions on novel mining and learning based solutions
in various aspects of legal data analysis such as legislations,
litigations, court cases, contracts, patents, NDAs and bylaws. Topics
of interest include, but are not limited to:

- Applications of Large Language Models (LLMs) and Generative AI in
the legal domain
    * Prompt engineering and automated prompting for legal NLP tasks
    * LLMs for legal contract drafting
    * Legal assistance using conversational AI
    * Risks and limitations of LLMs in the legal domain
- Applications of data mining techniques in the legal domain
    * Classifying, clustering, and identifying anomalies in big
corpora of legal records
    * Legal analytics
    * Citation analysis for case law
- Applications of machine learning and NLP techniques for legal textual data
    * Information extraction, information retrieval, question
answering and entity extraction/resolution for legal document reviews
    * Summarization of legal documents
    * eDiscovery in legal research
    * Case outcome prediction
    * Legal language modelling and legal document embedding and representation
    * Recommender systems for legal applications
    * Topic modeling in large amounts of legal documents
- Training data for legal domain
    * 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 and transfer learning, domain adaptation,
distant supervision
- Ethical issues in mining legal data
    * Privacy and GDPR in legal analytics
    * Bias and trust in the applications of data mining
    * Transparency in legal data mining
- Emerging topics in the intersection of AI and law
    * Digital lawyers and legal machines
    * Smart contracts
    * Future of law practice in the era of Generative AI

# Submission

All submissions must be in English, in PDF format, and in ACM two-column format (sigconf). The ACM LaTeX template are available from the ACM website ( and the Overleaf online editor (

To enable *double-blind reviewing*, authors are required to take all reasonable measures to conceal their identity. The `anonymous` option of the `acmart` class must be used.  Furthermore, ACM copyright and permission information should be removed by using the `nonacm` option. Therefore, the first line of your main LaTeX document should be as follows.

To facilitate the exchange of ideas, this year we adopt a policy similar to that of ICTIR'23 which allows submissions of *any length between 2 and 9 pages plus unrestricted space for references*. Authors are expected to submit a paper whose length reflects what is needed for the content of the work, i.e., page length should be commensurate with contribution size.  Reviewers will assess whether the contribution is appropriate for the given length. Consequently, there is no longer a distinction between long and short papers, nor a need of condensing or enlarging medium-length ones. We will probably allocate more presentation time to longer papers during the workshop.

As in the previous editions of MLLD, each paper will be reviewed by at least 3 reviewers from the Program Committee. 

We are going to produce *non-archival proceedings* for this workshop on Thus, authors can refine their accepted papers and submit them to formal conferences/journals after the workshop.

Submissions should be made electronically via EasyChair:

# Attendance

The CIKM-2023 conference will be held in-person in Birmingham, UK. Therefore, it is expected that most (if not all) of the authors will present their accepted papers in-person for this workshop. Some invited speakers and/or participants may have the flexibility to attend online.

# Contact

If you have any question regarding this workshop, please email [log in to unmask]

# Website


To unsubscribe from the DBWorld mailing list please visit and follow the unsubscribe directions given there.