Semantic disambiguation of scientific assertions in the form of nanopublications* is, thus far, under-utilised in scientific communication.
*nanoplublication: https://nanopub.net/
Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning
Wei Tang, Yin-Fang Yang, Weijia Zhang, Min-Ling Zhang
https://arxiv.org/abs/2512.17788 https://arxiv.org/pdf/2512.17788 https://arxiv.org/html/2512.17788
arXiv:2512.17788v1 Announce Type: new
Abstract: Multi-instance partial-label learning (MIPL) is a weakly supervised framework that extends the principles of multi-instance learning (MIL) and partial-label learning (PLL) to address the challenges of inexact supervision in both instance and label spaces. However, existing MIPL approaches often suffer from poor calibration, undermining classifier reliability. In this work, we propose a plug-and-play calibratable disambiguation loss (CDL) that simultaneously improves classification accuracy and calibration performance. The loss has two instantiations: the first one calibrates predictions based on probabilities from the candidate label set, while the second one integrates probabilities from both candidate and non-candidate label sets. The proposed CDL can be seamlessly incorporated into existing MIPL and PLL frameworks. We provide a theoretical analysis that establishes the lower bound and regularization properties of CDL, demonstrating its superiority over conventional disambiguation losses. Experimental results on benchmark and real-world datasets confirm that our CDL significantly enhances both classification and calibration performance.
toXiv_bot_toot
The talk section on the Wikipedia article about the acronym "MILF" is quite interesting. There have been heated discussions about age range and if having a child is actually a prerequisite.
Of course the article itself has a disambiguation that helps people who were actually looking for information about Moro Islamic Liberation Front. That article in turn has this line:
> MILF announced that it would disarm its 30,000 fighters.