·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Microsoft launches its first cybersecurity model, plus a new agentic cybersecurity system33m◆OpenAI called the Hugging Face attack unprecedented. But we’ve been here before.1h◆OpenAI’s Hugging Face breach has reignited the debate over alignment and control1h◆Why China is giving away its best AI models2h◆Threads users can now chat with Meta AI in their DMs2h◆Google’s AI search is rapidly becoming the default, new data shows3h◆Power up your AI infrastructure! A first look at the Smart Systems Stage agenda at TechCrunch Disrupt 20263h◆This $9 key physically locks your most addictive apps3h◆Ilya Sutskever’s Safe Superintelligence partners with Nvidia to scale its AI research4h◆Enigma raises $71M to make controlling a robot as easy as adjusting the volume6h◆Nvidia, Microsoft launch open AI security alliance — without OpenAI, Google, or Anthropic6h◆The path to artificial superintelligence7h◆Closing the data loop in AI-driven drug discovery7h◆Building the enterprise environment for agentic AI7h◆NVIDIA Cosmos-H-Dreams: Bringing Real-Time Generative Simulation to Surgical Robotics9h◆A Consensus-Based Framework for Relative Preference Evaluation of Large Language Models15h◆Probing Latent Colombian Identity Inferences in Qwen2.5-7B with Natural Language Autoencoders15h◆Data Quality over Capacity: Internalizing Documents into LoRA Adapters for Closed-Book QA15h◆Enjoy Your Talk: A Human-Centered Benchmark for Multi-Turn Dialogue with Decoupled User Simulation, Target Modeling, and Judging15h◆Multi-Mask Diffusion Language Models for Few-Step Generation15h◆Microsoft launches its first cybersecurity model, plus a new agentic cybersecurity system33m◆OpenAI called the Hugging Face attack unprecedented. But we’ve been here before.1h◆OpenAI’s Hugging Face breach has reignited the debate over alignment and control1h◆Why China is giving away its best AI models2h◆Threads users can now chat with Meta AI in their DMs2h◆Google’s AI search is rapidly becoming the default, new data shows3h◆Power up your AI infrastructure! A first look at the Smart Systems Stage agenda at TechCrunch Disrupt 20263h◆This $9 key physically locks your most addictive apps3h◆Ilya Sutskever’s Safe Superintelligence partners with Nvidia to scale its AI research4h◆Enigma raises $71M to make controlling a robot as easy as adjusting the volume6h◆Nvidia, Microsoft launch open AI security alliance — without OpenAI, Google, or Anthropic6h◆The path to artificial superintelligence7h◆Closing the data loop in AI-driven drug discovery7h◆Building the enterprise environment for agentic AI7h◆NVIDIA Cosmos-H-Dreams: Bringing Real-Time Generative Simulation to Surgical Robotics9h◆A Consensus-Based Framework for Relative Preference Evaluation of Large Language Models15h◆Probing Latent Colombian Identity Inferences in Qwen2.5-7B with Natural Language Autoencoders15h◆Data Quality over Capacity: Internalizing Documents into LoRA Adapters for Closed-Book QA15h◆Enjoy Your Talk: A Human-Centered Benchmark for Multi-Turn Dialogue with Decoupled User Simulation, Target Modeling, and Judging15h◆Multi-Mask Diffusion Language Models for Few-Step Generation15h◆
News/Before the Labels: How Dataset Construction Shapes Suicidality Detection in Clinical Text
arxiv
PublishedJune 20, 2026 at 4:00 AM
—neutral

Before the Labels: How Dataset Construction Shapes Suicidality Detection in Clinical Text

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2606.19637v1 Announce Type: cross Abstract: Clinical NLP increasingly relies on electronic health record (EHR) data to detect suicidal behaviors, treating clinical documentation as more reliable ground truth than social media. We argue that this framing obscures how EHR-based suicidality datas

Stay posted· Newsletter

A 5-min weekly brief — top movers, price watch, story of the week.

// no spam · unsubscribe one-click · free forever

Discussion
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#clinical-nlp#mental-health#data-quality#research

No replies yet. Be first.

Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#clinical-nlp#mental-health#data-quality#research

Related coverage

More from ARXIV
arxivA Consensus-Based Framework for Relative Preference Evaluation of Large Language Models15harxivProbing Latent Colombian Identity Inferences in Qwen2.5-7B with Natural Language Autoencoders15harxivData Quality over Capacity: Internalizing Documents into LoRA Adapters for Closed-Book QA15harxivEnjoy Your Talk: A Human-Centered Benchmark for Multi-Turn Dialogue with Decoupled User Simulation, Target Modeling, and Judging15h
The Bubble Brief
WEEKLY

Read clinical-nlp insights every Tuesday — top movers, new releases, story of the week.

// no spam · unsubscribe one-click · free forever

Originally published on arxiv ↗
HomeModelsNews