pwc-archive/papers-with-abstracts huggingface dataset:
CORA: A Deep Active Learning Covid-19 Relevancy Algorithm to Identify Core Scientific Articles
Authors: Zubair Afzal, Vikrant Yadav, Olga Fedorova, Vaishnavi Kandala, Janneke van de Loo, Saber A. Akhondi, Pascal Coupet, George Tsatsaronis
Date: 2020-12-01 00:00:00
Proceeding: EMNLP (NLP-COVID19) 2020 12
Tasks: Active Learning, Articles

Ever since the COVID-19 pandemic broke out, the academic and scientific research community, as well as industry and governments around the world have joined forces in an unprecedented manner to fight the threat. Clinicians, biologists, chemists, bioinformaticians, nurses, data scientists, and all of the affiliated relevant disciplines have been mobilized to help discover efficient treatments for the infected population, as well as a vaccine solution to prevent further the virus spread. In this combat against the virus responsible for the pandemic, key for any advancements is the timely, accurate, peer-reviewed, and efficient communication of any novel research findings. In this paper we present a novel framework to address the information need of filtering efficiently the scientific bibliography for relevant literature around COVID-19. The contributions of the paper are summarized in the following: we define and describe the information need that encompasses the major requirements for COVID-19 articles relevancy, we present and release an expert-curated benchmark set for the task, and we analyze the performance of several state-of-the-art machine learning classifiers that may distinguish the relevant from the non-relevant COVID-19 literature.
Frugal neural reranking: evaluation on the Covid-19 literature
Authors: Tiago Almeida, Sérgio Matos
Date: 2020-12-01 00:00:00
Proceeding: EMNLP (NLP-COVID19) 2020 12
Tasks: Information Retrieval, Reranking, Retrieval

The Covid-19 pandemic urged the scientific community to join efforts at an unprecedented scale, leading to faster than ever dissemination of data and results, which in turn motivated more research works. This paper presents and discusses information retrieval models aimed at addressing the challenge of searching the large number of publications that stem from these studies. The model presented, based on classical baselines followed by an interaction based neural ranking model, was evaluated and evolved within the TREC Covid challenge setting. Results on this dataset show that, when starting with a strong baseline, our light neural ranking model can achieve results that are comparable to other model architectures that use very large number of parameters.
COVID-19 Surveillance through Twitter using Self-Supervised and Few Shot Learning
Authors: Brandon Lwowski, Peyman Najafirad
Date: 2020-12-01 00:00:00
Proceeding: EMNLP (NLP-COVID19) 2020 12
Tasks: Few-Shot Learning

Public health surveillance and tracking virus via social media can be a useful digital tool for contact tracing and preventing the spread of the virus. Nowadays, large volumes of COVID-19 tweets can quickly be processed in real-time to offer information to researchers. Nonetheless, due to the absence of labeled data for COVID-19, the preliminary supervised classifier or semi-supervised self-labeled methods will not handle non-spherical data with adequate accuracy. With the seasonal influenza and novel Coronavirus having many similar symptoms, we propose using few shot learning to fine-tune a semi-supervised model built on unlabeled COVID-19 and previously labeled influenza dataset that can provide in- sights into COVID-19 that have not been investigated. The experimental results show the efficacy of the proposed model with an accuracy of 86%, identification of Covid-19 related discussion using recently collected tweets.
Explaining the Trump Gap in Social Distancing Using COVID Discourse
Authors: Austin Van Loon, Sheridan Stewart, Brandon Waldon, Shrinidhi K Lakshmikanth, Ishan Shah, Sharath Chandra Guntuku, Garrick Sherman, James Zou, Johannes Eichstaedt
Date: 2020-12-01 00:00:00
Proceeding: EMNLP (NLP-COVID19) 2020 12
Tasks: Word Embeddings

Our ability to limit the future spread of COVID-19 will in part depend on our understanding of the psychological and sociological processes that lead people to follow or reject coronavirus health behaviors. We argue that the virus has taken on heterogeneous meanings in communities across the United States and that these disparate meanings shaped communities’ response to the virus during the early, vital stages of the outbreak in the U.S. Using word embeddings, we demonstrate that counties where residents socially distanced less on average (as measured by residential mobility) more semantically associated the virus in their COVID discourse with concepts of fraud, the political left, and more benign illnesses like the flu. We also show that the different meanings the virus took on in different communities explains a substantial fraction of what we call the “”Trump Gap”, or the empirical tendency for more Trump-supporting counties to socially distance less. This work demonstrates that community-level processes of meaning-making in part determined behavioral responses to the COVID-19 pandemic and that these processes can be measured unobtrusively using Twitter.
COVIDLies: Detecting COVID-19 Misinformation on Social Media
Authors: Tamanna Hossain, Robert L. Logan IV, Arjuna Ugarte, Yoshitomo Matsubara, Sean Young, Sameer Singh
Date: 2020-12-01 00:00:00
Proceeding: EMNLP (NLP-COVID19) 2020 12
Tasks: Misconceptions, Misinformation, Retrieval, Stance Detection

The ongoing pandemic has heightened the need for developing tools to flag COVID-19-related misinformation on the internet, specifically on social media such as Twitter. However, due to novel language and the rapid change of information, existing misinformation detection datasets are not effective for evaluating systems designed to detect misinformation on this topic. Misinformation detection can be divided into two sub-tasks: (i) retrieval of misconceptions relevant to posts being checked for veracity, and (ii) stance detection to identify whether the posts Agree, Disagree, or express No Stance towards the retrieved misconceptions. To facilitate research on this task, we release COVIDLies (https://ucinlp.github.io/covid19 ), a dataset of 6761 expert-annotated tweets to evaluate the performance of misinformation detection systems on 86 different pieces of COVID-19 related misinformation. We evaluate existing NLP systems on this dataset, providing initial benchmarks and identifying key challenges for future models to improve upon.
Improved Topic Representations of Medical Documents to Assist COVID-19 Literature Exploration
Authors: Yulia Otmakhova, Karin Verspoor, Timothy Baldwin, Simon Šuster
Date: 2020-12-01 00:00:00
Proceeding: EMNLP (NLP-COVID19) 2020 12
Tasks: Specificity, Topic Models

Efficient discovery and exploration of biomedical literature has grown in importance in the context of the COVID-19 pandemic, and topic-based methods such as latent Dirichlet allocation (LDA) are a useful tool for this purpose. In this study we compare traditional topic models based on word tokens with topic models based on medical concepts, and propose several ways to improve topic coherence and specificity.
Automatic Evaluation vs. User Preference in Neural Textual QuestionAnswering over COVID-19 Scientific Literature
Authors: Arantxa Otegi, Jon Ander Campos, Gorka Azkune, Aitor Soroa, Eneko Agirre
Date: 2020-12-01 00:00:00
Proceeding: EMNLP (NLP-COVID19) 2020 12
Tasks: Information Retrieval, Question Answering, Reading Comprehension, Retrieval

We present a Question Answering (QA) system that won one of the tasks of the Kaggle CORD-19 Challenge, according to the qualitative evaluation of experts. The system is a combination of an Information Retrieval module and a reading comprehension module that finds the answers in the retrieved passages. In this paper we present a quantitative and qualitative analysis of the system. The quantitative evaluation using manually annotated datasets contradicted some of our design choices, e.g. the fact that using QuAC for fine-tuning provided better answers over just using SQuAD. We analyzed this mismatch with an additional A/B test which showed that the system using QuAC was indeed preferred by users, confirming our intuition. Our analysis puts in question the suitability of automatic metrics and its correlation to user preferences. We also show that automatic metrics are highly dependent on the characteristics of the gold standard, such as the average length of the answers.
Public Sentiment on Governmental COVID-19 Measures in Dutch Social Media
Authors: Shihan Wang, Marijn Schraagen, Erik Tjong Kim Sang, Mehdi Dastani
Date: 2020-12-01 00:00:00
Proceeding: EMNLP (NLP-COVID19) 2020 12
Tasks: Sentiment Analysis

Public sentiment (the opinion, attitude or feeling that the public expresses) is a factor of interest for government, as it directly influences the implementation of policies. Given the unprecedented nature of the COVID-19 crisis, having an up-to-date representation of public sentiment on governmental measures and announcements is crucial. In this paper, we analyse Dutch public sentiment on governmental COVID-19 measures from text data collected across three online media sources (Twitter, Reddit and Nu.nl) from February to September 2020. We apply sentiment analysis methods to analyse polarity over time, as well as to identify stance towards two specific pandemic policies regarding social distancing and wearing face masks. The presented preliminary results provide valuable insights into the narratives shown in vast social media text data, which help understand the influence of COVID-19 measures on the general public.
Twitter Data Augmentation for Monitoring Public Opinion on COVID-19 Intervention Measures
Authors: Lin Miao, Mark Last, Marina Litvak
Date: 2020-12-01 00:00:00
Proceeding: EMNLP (NLP-COVID19) 2020 12
Tasks: Data Augmentation

The COVID-19 outbreak is an ongoing worldwide pandemic that was announced as a global health crisis in March 2020. Due to the enormous challenges and high stakes of this pandemic, governments have implemented a wide range of policies aimed at containing the spread of the virus and its negative effect on multiple aspects of our life. Public responses to various intervention measures imposed over time can be explored by analyzing the social media. Due to the shortage of available labeled data for this new and evolving domain, we apply data distillation methodology to labeled datasets from related tasks and a very small manually labeled dataset. Our experimental results show that data distillation outperforms other data augmentation methods on our task.
COVID-19: A Semantic-Based Pipeline for Recommending Biomedical Entities
Authors: Marcia Afonso Barros, Andre Lamurias, Diana Sousa, Pedro Ruas, Francisco M. Couto
Date: 2020-12-01 00:00:00
Proceeding: EMNLP (NLP-COVID19) 2020 12
Tasks: Collaborative Filtering, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Recommendation Systems

With the increasing number of publications about COVID-19, it is a challenge to extract personalized knowledge suitable for each researcher. This work aims to build a new semantic-based pipeline for recommending biomedical entities to scientific researchers. To this end, we developed a pipeline that creates an implicit feedback matrix based on Named Entity Recognition (NER) on a corpus of documents, using multidisciplinary ontologies for recognizing and linking the entities. Our hypothesis is that by using ontologies from different fields in the NER phase, we can improve the results for state-of-the-art collaborative-filtering recommender systems applied to the dataset created. The tests performed using the COVID-19 Open Research Dataset (CORD-19) dataset show that when using four ontologies, the results for precision@k, for example, reach the 80%, whereas when using only one ontology, the results for precision@k drops to 20%, for the same users. Furthermore, the use of multi-fields entities may help in the discovery of new items, even if the researchers do not have items from that field in their set of preferences.
Vapur: A Search Engine to Find Related Protein - Compound Pairs in COVID-19 Literature
Authors: Abdullatif Köksal, Hilal Dönmez, Rıza Özçelik, Elif Ozkirimli, Arzucan Özgür
Date: 2020-12-01 00:00:00
Proceeding: EMNLP (NLP-COVID19) 2020 12

Coronavirus Disease of 2019 (COVID-19) created dire consequences globally and triggered an intense scientific effort from different domains. The resulting publications created a huge text collection in which finding the studies related to a biomolecule of interest is challenging for general purpose search engines because the publications are rich in domain specific terminology. Here, we present Vapur: an online COVID-19 search engine specifically designed to find related protein - chemical pairs. Vapur is empowered with a relation-oriented inverted index that is able to retrieve and group studies for a query biomolecule with respect to its related entities. The inverted index of Vapur is automatically created with a BioNLP pipeline and integrated with an online user interface. The online interface is designed for the smooth traversal of the current literature by domain researchers and is publicly available at https://tabilab.cmpe.boun.edu.tr/vapur/.
Knowledge Discovery in COVID-19 Research Literature
Authors: Alejandro Piad-Morffis, Suilan Estevez-Velarde, Ernesto Luis Estevanell-Valladares, Yoan Gutiérrez, Andrés Montoyo, Rafael Muñoz, Yudivián Almeida-Cruz
Date: 2021-09-01 00:00:00
Proceeding: RANLP 2021 9

This paper presents the preliminary results of an ongoing project that analyzes the growing body of scientific research published around the COVID-19 pandemic. In this research, a general-purpose semantic model is used to double annotate a batch of $500$ sentences that were manually selected by the researchers from the CORD-19 corpus. Afterwards, a baseline text-mining pipeline is designed and evaluated via a large batch of $100,959$ sentences. We present a qualitative analysis of the most interesting facts automatically extracted and highlight possible future lines of development. The preliminary results show that general-purpose semantic models are a useful tool for discovering fine-grained knowledge in large corpora of scientific documents.
Identifying pandemic-related stress factors from social-media posts – Effects on students and young-adults
Authors: Sachin Thukral, Suyash Sangwan, Arnab Chatterjee, Lipika Dey
Date: 2020-12-01 00:00:00
Proceeding: EMNLP (NLP-COVID19) 2020 12

The COVID-19 pandemic has thrown natural life out of gear across the globe. Strict measures are deployed to curb the spread of the virus that is causing it, and the most effective of them have been social isolation. This has led to wide-spread gloom and depression across society but more so among the young and the elderly. There are currently more than 200 million college students in 186 countries worldwide, affected due to the pandemic. The mode of education has changed suddenly, with the rapid adaptation of e-learning, whereby teaching is undertaken remotely and on digital platforms. This study presents insights gathered from social media posts that were posted by students and young adults during the COVID times. Using statistical and NLP techniques, we analyzed the behavioural issues reported by users themselves in their posts in depression related communities on Reddit. We present methodologies to systematically analyze content using linguistic techniques to find out the stress-inducing factors. Online education, losing jobs, isolation from friends and abusive families emerge as key stress factors
Tracking And Understanding Public Reaction During COVID-19: Saudi Arabia As A Use Case
Authors: Aseel Addawood, Alhanouf Alsuwailem, Ali Alohali, Dalal Alajaji, Mashail Alturki, Jaida Alsuhaibani, Fawziah Aljabli
Date: 2020-12-01 00:00:00
Proceeding: EMNLP (NLP-COVID19) 2020 12

The coronavirus disease of 2019 (COVID-19) has a huge impact on economies and societies around the world. While governments are taking extreme measures to reduce the spread of the virus, people are getting affected by these new measures. With restrictions like lockdown and social distancing, it became important to understand the emotional response of the public towards the pandemic. In this paper, we study the reaction of Saudi Arabia citizens towards the pandemic. We utilize a collection of Arabic tweets that were sent during 2020, primarily through hashtags that were originated from Saudi Arabia. Our results showed that people had kept a positive reaction towards the pandemic. This positive reaction was at its highest at the beginning of the COVID-19 crisis and started to decline as time passes. Overall, the results showed that people were so supportive of each other through this pandemic. This research can help researchers and policymakers in understanding the emotional effect of a pandemic on societies.
Annotating the Pandemic: Named Entity Recognition and Normalisation in COVID-19 Literature
Authors: Nico Colic, Lenz Furrer, Fabio Rinaldi
Date: 2020-12-01 00:00:00
Proceeding: EMNLP (NLP-COVID19) 2020 12
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

The COVID-19 pandemic has been accompanied by such an explosive increase in media coverage and scientific publications that researchers find it difficult to keep up. We are presenting a publicly available pipeline to perform named entity recognition and normalisation in parallel to help find relevant publications and to aid in downstream NLP tasks such as text summarisation. In our approach, we are using a dictionary-based system for its high recall in conjunction with two models based on BioBERT for their accuracy. Their outputs are combined according to different strategies depending on the entity type. In addition, we are using a manually crafted dictionary to increase performance for new concepts related to COVID-19. We have previously evaluated our work on the CRAFT corpus, and make the output of our pipeline available on two visualisation platforms.
AskMe: A LAPPS Grid-based NLP Query and Retrieval System for Covid-19 Literature
Authors: Keith Suderman, Nancy Ide, Verhagen Marc, Brent Cochran, James Pustejovsky
Date: 2020-12-01 00:00:00
Proceeding: EMNLP (NLP-COVID19) 2020 12
Tasks: Retrieval

In a recent project, the Language Application Grid was augmented to support the mining of scientific publications. The results of that ef- fort have now been repurposed to focus on Covid-19 literature, including modification of the LAPPS Grid “AskMe” query and retrieval engine. We describe the AskMe system and discuss its functionality as compared to other query engines available to search covid-related publications.
Concept Wikification for COVID-19
Authors: Panagiotis Lymperopoulos, Haoling Qiu, Bonan Min
Date: 2020-12-01 00:00:00
Proceeding: EMNLP (NLP-COVID19) 2020 12
Tasks: Articles

Understanding scientific articles related to COVID-19 requires broad knowledge about concepts such as symptoms, diseases and medicine. Given the very large and ever-growing scientific articles related to COVID-19, it is a daunting task even for experts to recognize the large set of concepts mentioned in these articles. In this paper, we address the problem of concept wikification for COVID-19, which is to automatically recognize mentions of concepts related to COVID-19 in text and resolve them into Wikipedia titles. We develop an approach to curate a COVID-19 concept wikification dataset by mining Wikipedia text and the associated intra-Wikipedia links. We also develop an end-to-end system for concept wikification for COVID-19. Preliminary experiments show very encouraging results. Our dataset, code and pre-trained model are available at github.com/panlybero/Covid19_wikification.
Developing a Curated Topic Model for COVID-19 Medical Research Literature
Authors: Philip Resnik, Katherine E. Goodman, Mike Moran
Date: 2020-12-01 00:00:00
Proceeding: EMNLP (NLP-COVID19) 2020 12
Tasks: Articles, Topic Models

Topic models can facilitate search, navigation, and knowledge discovery in large document collections. However, automatic generation of topic models can produce results that fail to meet the needs of users. We advocate for a set of user-focused desiderata in topic modeling for the COVID-19 literature, and describe an effort in progress to develop a curated topic model for COVID-19 articles informed by subject matter expertise and the way medical researchers engage with medical literature.
Collecting Verified COVID-19 Question Answer Pairs
Authors: Adam Poliak, Max Fleming, Cash Costello, Kenton Murray, Mahsa Yarmohammadi, Shivani Pandya, Darius Irani, Milind Agarwal, Udit Sharma, Shuo Sun, Nicola Ivanov, Lingxi Shang, Kaushik Srinivasan, Seolhwa Lee, Xu Han, Smisha Agarwal, João Sedoc
Date: 2020-12-01 00:00:00
Proceeding: EMNLP (NLP-COVID19) 2020 12
Tasks: Chatbot

We release a dataset of over 2,100 COVID19 related Frequently asked Question-Answer pairs scraped from over 40 trusted websites. We include an additional 24, 000 questions pulled from online sources that have been aligned by experts with existing answered questions from our dataset. This paper describes our efforts in collecting the dataset and summarizes the resulting data. Our dataset is automatically updated daily and available at https://github.com/JHU-COVID-QA/ scraping-qas. So far, this data has been used to develop a chatbot providing users information about COVID-19. We encourage others to build analytics and tools upon this dataset as well.
Using the Poly-encoder for a COVID-19 Question Answering System
Authors: Seolhwa Lee, João Sedoc
Date: 2020-12-01 00:00:00
Proceeding: EMNLP (NLP-COVID19) 2020 12
Tasks: Misinformation, Question Answering, Retrieval

To combat misinformation regarding COVID- 19 during this unprecedented pandemic, we propose a conversational agent that answers questions related to COVID-19. We adapt the Poly-encoder (Humeau et al., 2020) model for informational retrieval from FAQs. We show that after fine-tuning, the Poly-encoder can achieve a higher F1 score. We make our code publicly available for other researchers to use.
Hate and Toxic Speech Detection in the Context of Covid-19 Pandemic using XAI: Ongoing Applied Research
Authors: David Hardage, Peyman Najafirad
Date: 2020-12-01 00:00:00
Proceeding: EMNLP (NLP-COVID19) 2020 12
Tasks: Explainable Artificial Intelligence (XAI), Feature Importance, text-classification, Text Classification

As social distancing, self-quarantines, and travel restrictions have shifted a lot of pandemic conversations to social media so does the spread of hate speech. While recent machine learning solutions for automated hate and offensive speech identification are available on Twitter, there are issues with their interpretability. We propose a novel use of learned feature importance which improves upon the performance of prior state-of-the-art text classification techniques, while producing more easily interpretable decisions. We also discuss both technical and practical challenges that remain for this task.
Real-time Classification, Geolocation and Interactive Visualization of COVID-19 Information Shared on Social Media to Better Understand Global Developments
Authors: Andrei Mircea
Date: 2020-12-01 00:00:00
Proceeding: EMNLP (NLP-COVID19) 2020 12
Tasks: Classification

As people communicate on social media during COVID-19, it can be an invaluable source of useful and up-to-date information. However, the large volume and noise-to-signal ratio of social media can make this impractical. We present a prototype dashboard for the real-time classification, geolocation and interactive visualization of COVID-19 tweets that addresses these issues. We also describe a novel L2 classification layer that outperforms linear layers on a dataset of respiratory virus tweets.