The Wall Street Journal section of the Penn Treebank has been the de-facto standard for evaluating POS taggers for a long time, and accuracies over 97\% have been reported. However, less is known about out-of-domain tagger performance, especially with fine-grained label sets. Using data from Elder Scrolls Fandom, a wiki about the \textit{Elder Scrolls} video game universe, we create a modest dataset for qualitatively evaluating the cross-domain performance of two POS taggers: the Stanford tagger (Toutanova et al. 2003) and Bilty (Plank et al. 2016), both trained on WSJ. Our analyses show that performance on tokens seen during training is almost as good as in-domain performance, but accuracy on unknown tokens decreases from 90.37% to 78.37% (Stanford) and 87.84\% to 80.41\% (Bilty) across domains. Both taggers struggle with proper nouns and inconsistent capitalization.

Text Classification is the most essential and fundamental problem in Natural Language Processing. While numerous recent text classification models applied the sequential deep learning technique, graph neural network-based models can directly deal with complex structured text data and exploit global information. Many real text classification applications can be naturally cast into a graph, which captures words, documents, and corpus global features. In this survey, we bring the coverage of methods up to 2023, including corpus-level and document-level graph neural networks. We discuss each of these methods in detail, dealing with the graph construction mechanisms and the graph-based learning process. As well as the technological survey, we look at issues behind and future directions addressed in text classification using graph neural networks. We also cover datasets, evaluation metrics, and experiment design and present a summary of published performance on the publicly available benchmarks. Note that we present a comprehensive comparison between different techniques and identify the pros and cons of various evaluation metrics in this survey.

FOX Business correspondent Lydia Hu has the latest on jobs at risk as AI further develops on'America's Newsroom.' The advancement of artificial intelligence is progressing at a breakneck pace. While the technology is changing rapidly, the basic principles behind AI aren't new. Artificial intelligence has been around for many years, and has been built upon by many different developers. Today, some of the most well known AIs include chatbots like ChatGPT and Google Bard, with many more to come.

Fox News correspondent Grady Trimble has the latest on fears the technology will spiral out of control on'Special Report.' Artificial intelligence leader OpenAI has introduced the ability to turn off chat history in its popular chatbot ChatGPT. In a Tuesday blog post, the company said conversations that are started when chat history is disabled will not be used to train and improve its models and will not appear in the history sidebar. The controls are found in the ChatGPT settings and can be changed at any time. The mode rolled out ot all users.

In fact, a recent study named it the highest earning song on Spotify in the UK, despite it being released by The Killers 20 years ago. The track has so far brought the Las Vegas-born band more than £1 million in royalties ($1,254,087) through the streaming service. Indeed, since 2004, 'Mr Brightside' has spent 358 non-consecutive weeks in the UK singles charts, and is currently at number 62. But why exactly has Mr Brightside stood the test of time? MailOnline takes a look at the science behind this rock anthem.

Angie Wisdom and Dr. Chirag Shah discuss how artificial intelligence could play a role in online and professional relationships. Artificial intelligence (AI) is already providing significant benefit to people searching for love, negotiating a business deal and struggling with depression. However, according to experts, an over reliance on the technology could cause a "devaluation" of human connection and lead to diminishing authenticity. Rijul Gupta, CEO and co-founder of DeepMedia, told Fox News Digital that as AI continues to evolve, the technology has the potential to enrich social interactions, deliver companionship, and provide support in areas like child-rearing and elder care. Furthermore, Gupta predicted AI is set to "revolutionize" online dating by enhancing existing platforms, such as Tinder and Hinge, with new algorithms that provide more "personalized" and "efficient" matchmaking.

Log Off Movement CEO Emma Lembke and teacher Matt Miles discuss the impact of artificial intelligence on kids on'The Story.' Fifty-five percent of Americans are worried by the threat of AI to the future of humanity, according to a recent Monmouth University poll. More than 1,000 AI experts and funders, including Elon Musk and Steve Wozniak, signed a letter calling for a six-month pause in training new AI models. In turn, Time published an article calling for a permanent global ban. However, the problem with these proposals is that they require coordination of numerous stakeholders from a wide variety of companies and government figures. Let me share a more modest proposal that's much more in line with our existing methods of reining in potentially threatening developments: legal liability.

Grimes has welcomed musicians to create new songs with her voice using Artificial Intelligence, saying she would split 50% of royalties on any successful AI-generated track that included her voice. The Canadian singer, whose real name is Claire Boucher, tweeted that it was the "same deal as I would with any artist I collab[orate] with. Feel free to use my voice without penalty," she tweeted. I'll split 50% royalties on any successful AI generated song that uses my voice. Feel free to use my voice without penalty.

The rapid growth of social media has caused tremendous effects on information propagation, raising extreme challenges in detecting rumors. Existing rumor detection methods typically exploit the reposting propagation of a rumor candidate for detection by regarding all reposts to a rumor candidate as a temporal sequence and learning semantics representations of the repost sequence. However, extracting informative support from the topological structure of propagation and the influence of reposting authors for debunking rumors is crucial, which generally has not been well addressed by existing methods. In this paper, we organize a claim post in circulation as an adhoc event tree, extract event elements, and convert it to bipartite adhoc event trees in terms of both posts and authors, i.e., author tree and post tree. Accordingly, we propose a novel rumor detection model with hierarchical representation on the bipartite adhoc event trees called BAET. Specifically, we introduce word embedding and feature encoder for the author and post tree, respectively, and design a root-aware attention module to perform node representation. Then we adopt the tree-like RNN model to capture the structural correlations and propose a tree-aware attention module to learn tree representation for the author tree and post tree, respectively. Extensive experimental results on two public Twitter datasets demonstrate the effectiveness of BAET in exploring and exploiting the rumor propagation structure and the superior detection performance of BAET over state-of-the-art baseline methods.

Collaborative Filtering (CF) is a widely used and effective technique for recommender systems. In recent decades, there have been significant advancements in latent embedding-based CF methods for improved accuracy, such as matrix factorization, neural collaborative filtering, and LightGCN. However, the explainability of these models has not been fully explored. Adding explainability to recommendation models can not only increase trust in the decisionmaking process, but also have multiple benefits such as providing persuasive explanations for item recommendations, creating explicit profiles for users and items, and assisting item producers in design improvements. In this paper, we propose a neat and effective Explainable Collaborative Filtering (ECF) model that leverages interpretable cluster learning to achieve the two most demanding objectives: (1) Precise - the model should not compromise accuracy in the pursuit of explainability; and (2) Self-explainable - the model's explanations should truly reflect its decision-making process, not generated from post-hoc methods. The core of ECF is mining taste clusters from user-item interactions and item profiles.We map each user and item to a sparse set of taste clusters, and taste clusters are distinguished by a few representative tags. The user-item preference, users/items' cluster affiliations, and the generation of taste clusters are jointly optimized in an end-to-end manner. Additionally, we introduce a forest mechanism to ensure the model's accuracy, explainability, and diversity. To comprehensively evaluate the explainability quality of taste clusters, we design several quantitative metrics, including in-cluster item coverage, tag utilization, silhouette, and informativeness. Our model's effectiveness is demonstrated through extensive experiments on three real-world datasets.