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Knowledge injected prompt based fine-tuning for multi-label few-shot icd coding
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 …
codes to a medical note with average length of 3,000+ tokens. This task is challenging due …
XRR: Extreme multi-label text classification with candidate retrieving and deep ranking
J ** input texts to the most relevant subset of labels selected from an extremely …
Ngame: Negative mining-aware mini-batching for extreme classification
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 …
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
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 …
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
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 …
labels is referred to as Extreme Classification (XC). State-of-the-art XC methods have …
Extreme zero-shot learning for extreme text classification
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 …
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
Extreme classification (XC) involves predicting over large numbers of classes (thousands to
millions), with real-world applications like news article classification and e-commerce …
millions), with real-world applications like news article classification and e-commerce …