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Jun 21, 2024 · In noisy label learning, instance selection based on small-loss criteria has been proven to be highly effective.
The code for our paper "Noisy Multi-Label Text Classification via Instance-Label Pair Correction” (Accepted by NAACL'24).
Multi-label classification aims to learn classification models from instances associated with multiple labels. It is pivotal to learn and utilize the label ...
Oct 12, 2016 · It's a professional package created for finding labels errors in datasets and learning with noisy labels. It works with any scikit-learn model out-of-the-box.
Missing: Pair Correction.
Nov 7, 2021 · Large datasets in NLP tend to suffer from noisy labels due to erro- neous automatic and human annotation procedures. We study the problem of ...
Extreme multi-label text classification (XMTC) refers to the problem of assigning to each document its most relevant subset of class labels from an ...
Nov 2, 2022 · In this paper, we bring label dependence to tackle the problem of multi-label classification with noisy labels.
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Oct 3, 2024 · instance-label pair otj can be reconstructed using the weighted sum of the label ytj and the scores of the nearest neighbours of the t-th ...
Missing: Correction. | Show results with:Correction.
To address this problem, in this paper, we formulate the noise transition model in a Bayesian framework and subsequently design a new label correction algorithm ...
Feb 16, 2021 · Multi-label classification (MLC) is a generaliza- tion of standard classification where multiple la- bels may be assigned to a given sample.