In the U.S. historically, local newspapers drew their content largely from newswires

A diversity of tasks use language models trained on semantic similarity data. While there are a variety of datasets that capture semantic similarity, they are either constructed from modern web data or are relatively small datasets created in the past decade by human annotators.

In the U.S. historically, local newspapers drew their content largely from newswires

A diversity of tasks use language models trained on semantic similarity data. While there are a variety of datasets that capture semantic similarity, they are either constructed from modern web data or are relatively small datasets created in the past decade by human annotators. Historically, around half of articles in U.S. local newspapers came from newswires like the Associated Press. While local papers reproduced articles from the newswire, they wrote their own headlines, which form abstractive summaries of the associated articles. We then use deep neural methods to detect which articles are from the same underlying source, in the presence of substantial noise and abridgement.

This study advances the field of conflict forecasting by using text-based actor embeddings with transformer models to predict dynamic changes in violent conflict patterns at the actor level. More specifically, we combine newswire texts with structured conflict event data and leverage recent advances in Natural Language Processing (NLP) techniques to forecast escalations and de-escalations among conflicting actors, such as governments, militias, separatist movements, and terrorists. This new approach accurately and promptly captures the inherently volatile patterns of violent conflicts, which existing methods have not been able to achieve. To create this framework, we began by curating and annotating a vast international newswire corpus, leveraging hand-labeled event data from the Uppsala Conflict Data Program. By using this hybrid dataset, our models can incorporate the textual context of news sources along with the precision and detail of structured event data. This combination enables us to make both dynamic and granular predictions about conflict developments. We validate our approach through rigorous back-testing against historical events, demonstrating superior out-of-sample predictive power. We find that our approach is quite effective in identifying and predicting phases of conflict escalation and de-escalation, surpassing the capabilities of traditional models. By focusing on actor interactions, our explicit goal is to provide actionable insights to policymakers, humanitarian organizations, and peacekeeping operations in order to enable targeted and effective intervention strategies.

In the U.S. historically, local newspapers drew their content largely from newswires like the Associated Press. Historians argue that newswires played a pivotal role in creating a national identity and shared understanding of the world, but there is no comprehensive archive of the content sent over newswires. We reconstruct such an archive by applying a customized deep learning pipeline to hundreds of terabytes of raw image scans from thousands of local newspapers. The resulting dataset contains 2.7 million unique public domain U.S. newswire articles, written between 1878 and 1977. Locations in these articles are georeferenced, topics are tagged using customized neural topic classification, named entities are recognized, and individuals are disambiguated to Wikipedia using a novel entity disambiguation model. To construct the Newswire dataset, we first recognize newspaper layouts and transcribe around 138 millions structured article texts from raw image scans. We then use a customized neural bi-encoder model to de-duplicate reproduced articles, in the presence of considerable abridgement and noise, quantifying how widely each article was reproduced. A text classifier is used to ensure that we only include newswire articles, which historically are in the public domain. The structured data that accompany the texts provide rich information about the who (disambiguated individuals), what (topics), and where (georeferencing) of the news that millions of Americans read over the course of a century. We also include Library of Congress metadata information about the newspapers that ran the articles on their front pages. The Newswire dataset is useful both for large language modeling - expanding training data beyond what is available from modern web texts - and for studying a diversity of questions in computational linguistics, social science, and the digital humanities.

A recent study from Göttingen University found that "chatbot disclosure has a negative indirect effect on customer retention through mitigated trust for services with high criticality." Although chatbots are in wide use and provide material benefits for consumers such as 24/7 support, customers may still be suspicious of whether automated responses can get them the help they need. These findings, and others like them, are making some companies reconsider how they are going about implementing chatbot technology into their customer service – can they continue to "pretend" chatbots are real? Can customers handle the truth about chatbots? How do conversational AI chatbots change the game?

AI software provider C3.ai is collaborating with Microsoft (NasdaqGS:MSFT) to enhance its "global customer experience and elevate sales performance using intelligent cloud technology." To achieve this, C3.ai is adopting and deploying Microsoft's Dynamics 365 Sales and Teams, so as to "better prioritize workloads, enhance sales experiences with mixed reality, and manage customer needs with conversation intelligence and sentiment analysis." "We're looking forward to working with C3.ai to further its business goals with our intelligent cloud services. With Dynamics 365 at the center of its business transformation, the C3.ai team can streamline customer engagement across sales and customer service to bring a unique, tailored experience to its employees and customers." NVIDIA Corporation (NasdaqGS:NVDA) has completed the acquisition of computer networking firm Mellanox Technologies, Ltd. for $7 billion.