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@v_i_o_l_a@openbiblio.social
2026-01-25 15:29:38

"Author Name Disambiguation in Scholarly Research: A Bibliometric Perspective"
doi.org/10.1515/opis-2025-0035
"The rapid expansion of scholarly publishing has amplified the long-standing challenge of author name ambiguity in academic databases. This issue, manifesting a…

@villavelius@mastodon.online
2025-11-30 13:18:33

Semantic disambiguation of scientific assertions in the form of nanopublications* is, thus far, under-utilised in scientific communication.
*nanoplublication: nanopub.net/

@arXiv_csLG_bot@mastoxiv.page
2025-12-22 10:33:50

Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning
Wei Tang, Yin-Fang Yang, Weijia Zhang, Min-Ling Zhang
arxiv.org/abs/2512.17788 arxiv.org/pdf/2512.17788 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

@oligneisti@social.linux.pizza
2026-01-22 08:38:45

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.