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#classification

1 post1 participant0 posts today
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Holle Meding

📰 Classifying Genre in Historical Medical Periodicals

Next in line: Vera Danilova presents her work on genre classification in digitized periodicals from European patient organizations (1951–1990) using #LLMs as part of the #ActDisease project.

🔹 XLM-RoBERTa (UDM) led Q&A tasks with 32% more correct answers than mBERT/hmBERT.
🔹 hmBERT (UDM) topped Administrative classification (+16%)
🔹 CORE-based models excelled in legal genre prediction.

#DigitalHumanities @tuberlin #classification #NLP

Public
Osma Suominen

The first alpha version of Skosmos 3 has been published! This release provides a peek into the upcoming next major version of the Skosmos publishing tool for SKOS controlled vocabularies.

The release features a reimplemented front-end with a fresh layout and improved accessibility, as well as many architectural improvements and modernization of the codebase.

github.com/NatLibFi/Skosmos/re

#Skosmos#SKOS#thesaurus
Public
malte

Who's working the most higher-order #classification systems of things in the world? Systematics in biology work on classifying all living things. Every field has their own classification systems. Is there a field that integrates those classification systems to create a "taxonomy of everything"? When I search for that concept I'm pointed towards people working on #SemanticWeb - I'm curious if there are any good sources out there to read up on, especially actual attempts/examples! #taxonomy

Public
naught101

Is there a data structure that can sensibly handle multiple hierarchical classification systems?

e.g. an Orange, in terms of phylogeny is
Plantae->Eudicot->...->Citrus->sinensis

and in terms of usefulness, is
Thing->Food->fruit->orange
(and it could have multiple parents in this taxonomy, e.g. cleaning product)

Bonus points for cool visualisations of this kind information.

#data#dataScience#dataStructure
Public
The Inquisitive Biologist

New review: How is the living world organized and classified? Encyclopaedic in scope yet compelling to read, the deeply researched Kingdoms, Empires, & Domains is an 851-page behemoth that surveys 26 centuries of our changing understanding.

inquisitivebiologist.com/2025/

#Books #BookReview #Bookstodon #Scicomm #Classification #Genetics #HistoryOfScience #ScienceHistory #HistSci #Microbiology #Phylogenetics #ScalaNaturae #Taxonomy #Zoophyta @bookstodon

The Inquisitive Biologist · Book review – Kingdoms, Empires, & Domains: The History of High-Level Biological ClassificationHow is the living world organized and classified? Encyclopaedic in scope yet compelling to read, the deeply researched Kingdoms, Empires, & Domains is an 851-page behemoth that surveys 26 centu…
Public
Paul Houle

PolyMeme: Fine-Grained Internet Meme Sensing

(... I want this for my Mastodon reader)

mdpi.com/1424-8220/24/17/5456

MDPIPolyMeme: Fine-Grained Internet Meme SensingInternet memes are a special type of digital content that is shared through social media. They have recently emerged as a popular new format of media communication. They are often multimodal, combining text with images and aim to express humor, irony, sarcasm, or sometimes convey hatred and misinformation. Automatically detecting memes is important since it enables tracking of social and cultural trends and issues related to the spread of harmful content. While memes can take various forms and belong to different categories, such as image macros, memes with labeled objects, screenshots, memes with text out of the image, and funny images, existing datasets do not account for the diversity of meme formats, styles and content. To bridge this gap, we present the PolyMeme dataset, which comprises approximately 27 K memes from four categories. This was collected from Reddit and a part of it was manually labelled into these categories. Using the manual labels, deep learning networks were trained to classify the unlabelled images with an estimated error rate of 7.35%. The introduced meme dataset in combination with existing datasets of regular images were used to train deep learning networks (ResNet, ViT) on meme detection, exhibiting very high accuracy levels (98% on the test set). In addition, no significant gains were identified from the use of regular images containing text.
#cs#ai#ml
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