Abstract We introduce Prompt Tuned Embedding Classification (PTEC) for classifying
companies within an investment firm's proprietary industry taxonomy, supporting their …

Document classification is helpful for law professionals to improve content browsing and
retrieval. Pretrained Language Models, such as BERT, have become established for legal …

Prompt Tuning is emerging as a scalable and cost-effective method to fine-tune Pretrained
Language Models (PLMs), which are often referred to as Large Language Models (LLMs) …

Abstract Extreme Multi-label Classification (XMC) is the task of labeling documents with one
or more labels from a large set of classes. In the context of Legal Artificial Intelligence, XMC …

Multi-label classification is a supervised Machine Learning problem, which can assign zero
or more mutually non-exclusive class labels for an instance. It is different from the multi-class …

The multi-label automatic classification of scientific publications based on a pre-defined
taxonomy, also called automatic subject indexing is a continuing research endeavor with …

My research focuses on aligning human and machine cognition, driven by a mission to use
technology to enhance the humanities by improving productivity, efficiency, and harmony in …