Teaching Offered Winter Semester 2024/25

 

Master Practical Course - Legal Natural Language Processing Lab (IN2106)

Instructors: Shanshan Xu

Course language: English

6 SWS, 10 ECTS

Session Times: TBA

Information Session

17:00, Wed, 10. July. 2024 

Meeting Recording:

tum-conf.zoom-x.de/rec/share/J4cYzXUM5f2VIixRmJcUslGDp-KXBgldgRsp4Yh6NMT1ct_bG2jt_1Ek05WHaQgC.SG9XdWKyjnNCv24e
Passcode: cqW$3?Xm

 

[IMPORTANT]

Following is a short questionnaire meant to pre-assess your background in ML and NLP suffices for the legal data analysis lab, and to provide us with information on how to rank applicants since we plan to only have 12 slots available. If you are interested in the lab and would like to match with us, please fill out the form. If you are decently proficient in Python, have some practical ML experience (e.g., by implementing a classifier) and can answer the "how familiar are you with" questions positively, you should be able to successfully complete the lab.

Questionnaire Link 

Content Outline

The analysis of legal data/text and the design and development of systems that provide valuable functionality to legal practitioners pose various challenges. These include noisy raw data that must be carefully preprocessed, ill-defined tasks for which only small datasets exist and for which learning supervision and evaluation is difficult to obtain, and domain-specific information of various kinds that must be taken into account at many stages of the process.

This lab course provides students with an opportunity to gain practical experience in working with legal data in small teams. The instructors will be offering projects centered around a research question/hypothesis. They will typically involve one or more datasets from a legal domain, one or more formal tasks, and one or more methods to be tried. Over the course of the semester, teams will develop an experimental system/prototype and evaluate it, thereby producing new insight about that hypothesis.

After an initial introduction of the legal informatics topic, students will be matched into teams and assigned projects. Teams will meet with their project mentors regularly to present work updates, discuss progress, and define action items. At the end of three milestone intervals, teams will present their progress to the whole cohort and discuss all projects with their peers.

Learning Outcomes

After completing this module, students will have gained practice in planning, implementing, and evaluating a legal data science/informatics project. In particular, they will have gained experience in:

• formulating an experimental hypothesis
• identifying characteristics of data from the legal domain and explain how they influence technical aspects of project work
• conduct a targeted prior work survey in the legal informatics literature for a given project context
• designing an experimental system towards producing insight from data and/or developing new functionality of interest
• conducting model evaluation and behavior analysis

Requirements

Students must have experience in machine learning and, ideally, natural language processing. They should have taken the following courses or be sufficiently proficient in the topics and methods they cover:

• IN2332: Statistical Modeling and Machine Learning
• IN2062: Grundlagen der künstlichen Intelligenz / Foundations of Artificial Intelligence
• IN2361: Natural Language Processing
• IN2395: Legal Data Science & Informatics

If a student has not taken IN2395, it is expected that they familiarize themselves with background materials relevant to their respective project.

 

 

 

 

Advanced Seminar: Topics in Natural Legal Language Processing (IN2107)

 

Instructors: Prof. Matthias Grabmair

Course language: English

2 SWS, 4 ECTS

Session Times: TBA

Information Session

Meeting Recording:

https://tum.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=98da5f72-9fab-4cee-85d2-b1aa00b22fef

 

[IMPORTANT]

Following is a short questionnaire meant to pre-assess your background in NLP suffices for the seminar, and to provide us with information on how to rank applicants since we have limited slots available. If you are interested in the seminar and would like to match with us, please fill out the form. Questionnaire Link 

Content Outline

Advances in Natural Language Processing (NLP) technology are increasingly absorbed into support systems for legal practitioners. At the same time, the legal domain, and legal text in particular, move into the focus of mainstream NLP research as a source of challenging problems and opportunities to make a real world impact.

This seminar will offer a curated set of topics in natural language processing of legal text for students to explore and distill into a paper and presentation. The expectation is that seminar contributions should investigate and discuss (1) how legal NLP tasks are similar/different to those explored in mainstream NLP, (2) what technical challenges arise when tackling particular tasks, (3) how NLP methods and models behave on legal data, (4) what non-legal mainstream NLP literature is relevant for the topic, and (5) what implications the obtained results have for practical application and future research. Seminar papers and presentations should critically discuss prior work in depth. In order to do this effectively within the scope of the seminar, students should have prior knowledge in NLP. Some seminar topics may include basic technical work (e.g., testing large language models with prompts, exploring a dataset, comparing model configurations, etc.).

·       Representative examples of seminar topics may include:

·       Case outcome prediction in different jurisdictions and model architectures

·       State of the art in pretraining of large legal language models

·       The past, present, and future of legal argument mining

·       Automatic compliance checking of legal requirements

·       State of the art in legal search engines

·       Potentials and Limits of Reasoning Abilities in Large Language Models

·       State of the art in non-English legal NLP for selected tasks

Students will conduct a survey of academic research literature around a given topic in natural legal language processing, write a scientific survey about the gained insights, and present it to, and discuss it with, other seminar participants. Grading will be based on the survey document and presentation.

Learning Outcomes

After completing this module, students will have gained experience in conducting a literature survey around a given topic in natural legal language processing, writing a survey paper according to academic standards, and present/discuss their findings with peers. In particular, they will have gained experience in:

·       Searching and collecting relevant source material for a topic

·       Curating the material for inclusion into the paper

·       Critically reading, and reflecting on, relevant research literature

·       Distilling the insights gained from the survey into a seminar paper narrative

·       Writing a scientific seminar paper according to academic standards

·       Prepare and conduct a presentation, and discussion, of the findings

Requirements

No strict course requirements, but experience in Natural Language Processing is necessary to succeed at the lab. Recommended prior courses are:

·       IN2332: Statistical Modeling and Machine Learning

·       IN2062: Grundlagen der k√ºnstlichen Intelligenz / Foundations of Artificial Intelligence

·       IN2361: Natural Language Processing

·       IN2395: Legal Data Science & Informatics

·      IN2106: Legal Natural Language Processing Lab (Praktikum)