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The COVID-19 pandemic has affected millions of people worldwide with severe health, economic, social, and political implications. Healthcare Policy Makers (HPM) and medical experts are at the core of responding to this continuously evolving pandemic situation and are working hard to restrain the spread and severity of this relatively unknown virus. Biomedical researchers are continually discovering new information about this virus and communicating the findings through scientific articles. As such, it is crucial for HPM and funding agencies to monitor the COVID-19 research trend globally on a regular basis. However, given the influx of biomedical research articles, monitoring COVID-19 research trends has become more challenging than ever, especially when HPMs want on-demand guided search techniques with a set of topics of interest in their minds. Unfortunately, existing topic trend modeling techniques are unable to serve this purpose as 1) Traditional topic models are unsupervised, and 2) HPMs in different regions may have different topics of interest that they want to track. To address this problem, we introduce a novel computational task in this paper called Ad-Hoc Topic Tracking , which is essentially a combination of zero-shot topic categorization and the Spatio-temporal analysis task. We then propose multiple zero-shot classification methods to solve this task by extending upon the state-of-the-art language understanding techniques. Next, we picked the best-performing method based on its accuracy on a separate validation data set and then applied it to a corpus of recent biomedical research articles to track Covid-19 research endeavors across the globe using a Spatio-Temporal analysis. A demo website has also been developed for HPMs to create custom Spatio-Temporal visualizations of COVID-19 research trends. The research outcomes demonstrate that the proposed zero-shot classification methods can potentially facilitate further research on this important subject matter, and at the same time, the Spatio-temporal visualization tool will greatly assist HPMs and funding agencies in making well-informed policy decisions for advancing scientific research efforts.

Many new books get published every year, and only a fraction of them become popular among the readers. So the prediction of a book success can be a very useful parameter for publishers to make a reliable decision. This article presents the study of semantic word associations using the word embedding of book content for a set of Roget's thesaurus concepts for book success prediction. In this work, we discuss the method to represent a book as a spectrum of concepts based on the association score between its content embedding and a global embedding (i.e. fastText) for a set of semantically linked word clusters. We show that the semantic word associations outperform the previous methods for book success prediction. In addition, we present that semantic word associations also provide better results than using features like the frequency of word groups in Roget's thesaurus, LIWC (a popular tool for linguistic inquiry and word count), NRC (word association emotion lexicon), and part of speech (PoS). Our study reports that concept associations based on Roget's Thesaurus using word embedding of individual novel resulted in the state-of-the-art performance of 0.89 average weighted F1-score for book success prediction. Finally, we present a set of dominant themes that contribute towards the popularity of a book for a specific genre.

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