A Meta-framework for Spatiotemporal Quantity Extraction from Text

News events are often associated with quantities (e.g., the number of COVID-19 patients or the number of arrests in a protest), and it is often important to extract their type, time, and location from unstructured text in order to analyze these quantity events. This paper thus formulates the NLP problem of spatiotemporal quantity extraction, and proposes the first meta-framework for solving it. This meta-framework contains a formalism that decomposes the problem into several information extraction tasks, a shareable crowdsourcing pipeline, and transformer-based baseline models. We demonstrate the meta-framework in three domains-the COVID-19 pandemic, Black Lives Matter protests, and 2020 California wildfires-to show that the formalism is general and extensible, the crowdsourcing pipeline facilitates fast and high-quality data annotation, and the baseline system can handle spatiotemporal quantity extraction well enough to be practically useful. We release all resources for future research on this topic. © 2022 Association for Computational Linguistics.

A Deep Neural Network for the Detection of COVID-19 from Chest X-ray Images

The most successful machine learning technology considered for analyzing a significant amount of chest X-ray images is Deep Learning and it has the potential to cause significant influence on Covid-19 screening. In this paper, we analyze four distinct Convolutional Neural Network (CNN) state-of-the-art architectures that are Baseline Model, Vanilla CNN, VGG-16 and Siamese Model on the basis of test accuracy. The effectiveness of the models under consideration is assessed using the Chest Radiograph dataset, which is publicly available for research. In order to discover COVID-19, we used well-known deep learning algorithms for data rarity. These include employing Siamese networks using transfer learning and a few-shot learning approach. Our experiments show that using few-shot learning methodologies, we can create a COVID-19 identification model that is both efficient and effective even with limited data. With this strategy, we were able to achieve 95% accuracy, compared to 86% with Baseline model. © 2022 Bharati Vidyapeeth, New Delhi.

Mobile-based Application for COVID-19 Detection from Lung X-Ray Scans with Artificial Neural Networks (ANN)

In early 2020, the World Health Organization (WHO) identified a novel coronavirus referred to as SARS-CoV-2, which is associated with the now commonly known COVID-19 disease. COVID-19 was shortly later characterized as a pandemic. All countries around the globe have been severely affected and the disease has accumulated a total of over 200 million cases and more than five million deaths in the past two years. Symptoms associated with COVID-19 vary greatly in severity. Some infected with COVID-19 are asymptomatic, while others experience critical disease with life-threatening complications. In this paper, a mobile-based application has been created to help classify Covid-19 and non-Covid-19 lung when given an image of a Chest X-Ray (CXR). A variety of different artificial neural networks (ANN) including our baseline model, InceptionV3, MobileNetV2, MobileNetV3, VGG16, and VGG19 were tested to see which would provide the optimal results. It is concluded that MobileNetV3 gives the best test accuracy of 95.49% and is considered a lightweight model suitable for a mobile-based application. © 2022 IEEE.

Condensed Discriminative Question Set for Reliable Exam Score Prediction

The inevitable shift towards online learning due to the emergence of the COVID-19 pandemic triggered a strong need to assess students using shorter exams whilst ensuring reliability. This study explores a data-centric approach that utilizes feature importance to select a discriminative subset of questions from the original exam. Furthermore, the discriminative question subset’s ability to approximate the students exam scores is evaluated by measuring the prediction accuracy and by quantifying the error interval of the prediction. The approach was evaluated using two real-world exam datasets of the Scholastic Aptitude Test (SAT) and Exame Nacional do Ensino Médio (ENEM) exams, which consist of student response data and the corresponding the exam scores. The evaluation was conducted against randomized question subsets of sizes 10, 20, 30 and 50. The results show that our method estimates the full scores more accurately than a baseline model in most question sizes while maintaining a reasonable error interval. The encouraging evidence found in this paper provides support for the strong potential of the on-going study to provide a data-centric approach for exam size reduction. © 2021, Springer Nature Switzerland AG.

Covid Vaccine Sentiment Analysis by Geographic Region

In this paper, we provide a sentiment analysis of conversations surrounding Covid-19 vaccine adoption on Twitter. We focus on key regions of the US, particularly urban areas with high African American populations. We utilize machine learning models such as logistic regression, Support Vector Machines, and Naive Bayes to provide baseline models. Furthermore, we develop fined-tuned Transformer-based language models that provide a classification of sentiments with high accuracy. The results from our analysis show that fine-tuning our dataset on a Transformer-based model, Covid-BERT v2, performs better than our baseline models however the accuracy is still relatively low. This might be as a result of the very limited training dataset. Future work will explore the use of a higher quality dataset and also evaluate other transformer-based models. © 2021 IEEE.

Claim Detection in Biomedical Twitter Posts

Social media contains unfiltered and unique information, which is potentially of great value, but, in the case of misinformation, can also do great harm. With regards to biomedical topics, false information can be particularly dangerous. Methods of automatic fact-checking and fake news detection address this problem, but have not been applied to the biomedical domain in social media yet. We aim to fill this research gap and annotate a corpus of 1200 tweets for implicit and explicit biomedical claims (the latter also with span annotations for the claim phrase). With this corpus, which we sample to be related to COVID-19, measles, cystic fibrosis, and depression, we develop baseline models which detect tweets that contain a claim automatically. Our anal-yses reveal that biomedical tweets are densely populated with claims (45 % in a corpus sampled to contain 1200 tweets focused on the domains mentioned above). Baseline classification experiments with embedding-based classifiers and BERT-based transfer learning demonstrate that the detection is challenging, however, shows acceptable performance for the identification of explicit expressions of claims. Implicit claim tweets are more challenging to detect. © 2021 Association for Computational Linguistics

Automatic Rephrasing of Transcripts-based Action Items

The automated transcription of spoken language, and meetings, in particular, is becoming more widespread as automatic speech recognition systems are becoming more accurate. This trend has significantly accelerated since the outbreak of the COVID-19 pandemic, which led to a major increase in the number of online meetings. However, the transcription of spoken language has not received much attention from the NLP community compared to documents and other forms of written language. In this paper, we study a variation of the summarization problem over the transcription of spoken language: given a transcribed meeting, and an action item (i.e., a commitment or request to perform a task), our goal is to generate a coherent and self-contained rephrasing of the action item. To this end, we compiled a novel dataset of annotated meeting transcripts, including human rephrasing of action items. We use state-of-the-art supervised text generation techniques and establish a strong baseline based on BART and UniLM (two pretrained transformer models). Due to the nature of natural speech, language is often broken and incomplete and the task is shown to be harder than an analogous task over email data. Particularly, we show that the baseline models can be greatly improved once models are provided with additional information. We compare two approaches: one incorporating features extracted by coreference-resolution. Additional annotations are used to train an auxiliary model to detect the relevant context in the text. Based on the systematic human evaluation, our best models exhibit near-human-level rephrasing capability on a constrained subset of the problem. © 2021 Association for Computational Linguistics