Knowledge injected prompt based fine-tuning for multi-label few-shot icd coding

Z Yang, S Wang, BPS Rawat… - Proceedings of the …, 2022 - pmc.ncbi.nlm.nih.gov
Automatic International Classification of Diseases (ICD) coding aims to assign multiple ICD
codes to a medical note with average length of 3,000+ tokens. This task is challenging due …

Ngame: Negative mining-aware mini-batching for extreme classification

K Dahiya, N Gupta, D Saini, A Soni, Y Wang… - Proceedings of the …, 2023 - dl.acm.org
Extreme Classification (XC) seeks to tag data points with the most relevant subset of labels
from an extremely large label set. Performing deep XC with dense, learnt representations for …

Generalized test utilities for long-tail performance in extreme multi-label classification

E Schultheis, M Wydmuch… - Advances in …, 2023 - proceedings.neurips.cc
Extreme multi-label classification (XMLC) is the task of selecting a small subset of relevant
labels from a very large set of possible labels. As such, it is characterized by long-tail labels …

Deep encoders with auxiliary parameters for extreme classification

K Dahiya, S Yadav, S Sondhi, D Saini… - Proceedings of the 29th …, 2023 - dl.acm.org
The task of annotating a data point with labels most relevant to it from a large universe of
labels is referred to as Extreme Classification (XC). State-of-the-art XC methods have …

Extreme zero-shot learning for extreme text classification

Y **ong, WC Chang, CJ Hsieh, HF Yu… - arxiv preprint arxiv …, 2021 - arxiv.org
The eXtreme Multi-label text Classification (XMC) problem concerns finding most relevant
labels for an input text instance from a large label set. However, the XMC setup faces two …

Semsup-xc: semantic supervision for zero and few-shot extreme classification

P Aggarwal, A Deshpande… - … on Machine Learning, 2023 - proceedings.mlr.press
Extreme classification (XC) involves predicting over large numbers of classes (thousands to
millions), with real-world applications like news article classification and e-commerce …