Goto

Collaborating Authors

Results


TimeLMs: Diachronic Language Models from Twitter

arXiv.org Artificial Intelligence

Despite its importance, the time variable has been largely neglected in the NLP and language model literature. In this paper, we present TimeLMs, a set of language models specialized on diachronic Twitter data. We show that a continual learning strategy contributes to enhancing Twitter-based language models' capacity to deal with future and out-of-distribution tweets, while making them competitive with standardized and more monolithic benchmarks. We also perform a number of qualitative analyses showing how they cope with trends and peaks in activity involving specific named entities or concept drift.


Analyzing COVID Medical Papers with Azure Machine Learning and Text Analytics for Health

#artificialintelligence

Since the beginning of the COVID pandemic, there have been more than 700000 scientific papers published on the subject. A human researcher cannot possibly get acquainted with such a huge text corpus — and therefore some help from AI is highly needed. In this post, we will show how we...


Analyzing COVID Medical Papers with Azure Machine Learning and Text Analytics for Health

#artificialintelligence

Before making this call, you need to create TextAnalyticsClient object, passing your endpoint and access key. You get those values from cognitive services/text analytics Azure resource that you need to create in your Azure Subscription through the portal or via command-line.