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TempEL: Linking Dynamically Evolving and Newly Emerging Entities

Neural Information Processing Systems

The dataset and the baseline code will be made publicly available in a dedicated GitHub repository upon acceptance. License TempEL is distributed under Creative Commons Attribution-ShareAlike 4.0 International license (CCBY-SA 4.0).1 Maintenance The maintenance and extension to further temporal snapshots of TempEL will be carried out by the authors of the paper. Additionally, we will make the code public to create potential new variations and extensions of TempEL using a number of hyperparameters (see Sections A.4 and A.5 for further details). A.2 Datasheet for TempEL In this section we provide a more detailed documentation of the dataset with the intended uses. We base ourselves on the datasheet proposed by [1]. A.2.1 Motivation For what purpose was the dataset created? The TempEL dataset was created to evaluate how the temporal change of anchor mentions and that of target Knowledge Base (KB; i.e., modification or creation of new entities) affects the entity linking (EL) task. This contrasts with the currently existing datasets [9, 7, 8, 6], which are associated with a single version of the target KB such as the Wikipedia 2010 for the widely adopted CoNLL-AIDA[2] dataset. We expect that TempEL will encourage research in devising new models and architectures that are robust to temporal changes both in mentions as well as in the target KBs. Who created the dataset and on behalf of which entity?




'The View' hosts blast RFK Jr's leadership as Joy Behar says policies are 'trying to kill us'

FOX News

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I put Microsoft's new Copilot tools to work in Office. It performed like an eager intern

PCWorld

PCWorld reports Microsoft 365 Copilot has evolved from offering passive suggestions to actively making live changes in Excel, PowerPoint, and Word documents. The upgraded agentic capabilities allow Copilot to create presentations and documents from scratch, though with some limitations like missing graphics. These enhanced features are available across Microsoft 365 Copilot, Premium, Personal, and Family subscriptions, representing a significant productivity upgrade. Although Microsoft's Copilot reportedly remains far behind competing AI Large Language Models (LLMs) in terms of usage, the Copilot built into its Microsoft 365 applications remains a potent assistant.



Continuous Mean-Covariance Bandits

Neural Information Processing Systems

Existing risk-aware multi-armed bandit models typically focus on risk measures of individual options such as variance. As a result, they cannot be directly applied to important real-world online decision making problems with correlated options. In this paper, we propose a novel Continuous Mean-Covariance Bandit (CMCB) model to explicitly take into account option correlation. Specifically, in CMCB, there is a learner who sequentially chooses weight vectors on given options and observes random feedback according to the decisions. The agent's objective is to achieve the best trade-off between reward and risk, measured with option covariance.