E xcavator C ovid: Extracting Events and Relations from Text Corpora for Temporal and Causal Analysis for COVID -19 (2021)

Abstract: Timely responses from policy makers to mitigate the impact of the COVID-19 pandemic rely on a comprehensive grasp of events, their causes, and their impacts. These events are reported at such a speed and scale as to be overwhelming. In this paper, we present ExcavatorCovid, a machine reading system that ingests open-source text documents (e.g., news and scientific publications), extracts COVID-19 related events and relations between them, and builds a Temporal and Causal Analysis Graph (TCAG). Excavator will help government agencies alleviate the information overload, understand likely downstream effects of political and economic decisions and events related to the pandemic, and respond in a timely manner to mitigate the impact of COVID-19. We expect the utility of Excavator to outlive the COVID-19 pandemic: analysts and decision makers will be empowered by Excavator to better understand and solve complex problems in the future. A demonstration video is available at https://vimeo.com/528619007 .

## introduction timely responses from policy makers to mitigate the impact of the covid-19 pandemic rely on a comprehensive grasp of events, their causes, and their impacts. since the beginning of the covid-19 pandemic, an enormous amount of articles are being published every day, that report many events foot_0 and studies related to covid. it is very difficult, if not impossible, to keep track of these developing events or to get a comprehensive overview of the temporal and causal dynamics underlying these events. to aid the policy makers in overcoming the information overload, we developed excavatorcovid (or excavator for short), a system that will ingest open-source text sources (e.g., news articles and scientific publications), extract covid-19 related events and relations between them, and build a temporal and causal analysis graph (tcag). excavator combines the following nlp techniques: excavator produces a tcag that is in a machine-readable json format and is also humanunderstandable (visualized via a web-based interactive user interface), to support varied analytical and decision making needs. we hope that excavator will aid government agencies in efforts to understand likely downstream effects of political and economic decisions and events related to the pandemic, and respond in a timely manner to mitigate the impact of covid-19. the benefit of excavator is realized through a comprehensive visualization of events and how they affect each other. we expect the utility of excavator to outlive the covid-19 pandemic: analysts and decision makers will be empowered by excavator to better understand and solve complex problems in the future. we first present our covid-19 event taxonomy, and then we present details about event extraction, causal and temporal relation extraction, measuring event popularity using news text as "quantitative data", and the approach for constructing a tcag. we then describe the system demonstration, present a quantitative analysis of the extractions, and conclude with recommended use cases. the event taxonomy includes 76 event types. each type comes with a name and a short description. figure 1 illustrates several branches of the event taxonomy 2 . the events come from a wide range of domains. we also manually added hyponymy relations via is_a links (e.g., covid-19 is_a virus) between pairs of event types. ## extracting events we developed a neural network model for extracting events defined in the covid-19 event taxonomy (the event classification stage) and extracting 2 the complete taxonomy is available at https: //github.com/bbn-e/learnit/blob/master/ inputs/domains/cord_19/ontology/covid_ event_ontology.yaml. the location and time arguments (the event argument extraction stage), if they are mentioned in text, for each event mention. the structured representation (events with location and/or time) enables analyses of events targeting a specific time or location. both stages use a bert-based sequence tagging model. figure 2(a) shows the model architecture. given a sequence of tokens as input, the model extracts a sequence of tags, one per each token. we use the commonly used begin-inside-outside (bio) tags for both event types and event argument role types for the event classification and argument attachment tasks respectively. event classification: a sequence tagging model is trained to predict bio tags of event types such that it identifies the event trigger span as well as the event type. tags of argument role types, such that it identifies token spans of event arguments as well as their argument role types, with respect to a trigger that has already been identified in the event classification stage and marked in the input sentence in "< @xmath0... < /@xmath1. figure 2 (c) shows an example. we run these two models in a pipeline: the event classification model is applied first to find event triggers and classify their types, then the event argument extraction model is applied to find location and time arguments for each event mention. training data curation. we use learnit rapid customization for event extraction @xcite to curate a dataset for training the event classification model. our developer spent about 13 minutes per event type to find, expand, and filter potential event triggers in a held-out 10% of the aylien coronavirus news corpus. statistics of the curated data set are shown in to train the argument extraction model, we use the related event-argument annotation from the ace 2005 dataset @xcite . we focus on location and time arguments foot_2 and ignore other roles. at decoding time, after extracting the argument mentions for events, we apply the awake @xcite entity linking system to resolve each location argument to a canonical geolocation, and use serif @xcite to resolve each time argument to a canonical time and then convert it to the month level. this allows us to perform analyses of events targeting a specific geolocation or month of interest. ## extracting temporal and causal relations we develop two approaches for extracting temporal and causal relations: a pattern-based approach and a neural network model. we take the union of the type subtype definition causes cause y happens because of x. catalyst if x, intensity of y increases. precondition x must have occured for y to happen. mitigates mitigation if x, intensity of y decreases. preventative if x happens, y can't happen. before before/after x happens before/after y. ## measuring event popularity through time the tcag only provides a qualitative analysis of the temporal and causal relations between the covid-related events. it will be more informative if we can measure the popularity of events through time to enable trend analysis (e.g., does lockdown go up or down between january and may, 2020?) and correlation analysis (e.g., will a stricter lockdown improve or deteriorate the economy?). in order to support these analyses, we produce a timeseries of a popularity score for each event type over time (a.k.a., event timeline). extending our prior work @xcite , we define the popularity score for event type e at time t as: p opularity(e) @xmath2,t+ t 2 ] n e,t cm t in which n e,t is the frequency of event e at month t. we calculate the moving average centered at each t with a sliding window of @xmath3. m t is the total number of articles published in month t. @xmath4. the raw event frequency counts can be inflated due to the increasing level of media activity. therefore, we divide the raw counts by cm t to normalize the counts so that they are comparable across different months. the second corpus is the covid-19 open research dataset @xcite . it contains coronavirus-related research from pubmed's pmc corpus, a corpus maintained by the who, and biorxiv and medrxiv pre-prints. as of 11/08/2020, it contains over 300,000 scholarly articles. we combine these two corpora because news and research articles are complementary: news are rich in real-world events and are up to date, while analytical articles contain more causal relationships. therefore, combining them is likely to lead to a more comprehensive analysis and new insights. overall statistics of extractions. excavator extracted 6.2 million event mentions of 59 types. ## recommended use cases we describe 3 recommended use cases below. more details are in our demonstration video. use case 1: causal and temporal analysis. we can get a panoramic view of the underlying casual and temporal dynamics between events related to covid from the overall tcag. we can start by analyzing the causal or temporal relations centered at an event of interest. for example, figure 4 shows a diverse range of effects and consequences of lockdown, such as economiccrisis (economic), shortage (supply-chain), fearorpanic (mental), etc. interestingly, the graph also reveals surprises such as {lockdown, causes, death}: the ui shows supporting evidence such as "lockdown exacerbates deaths and chronic health problems associated with poverty, ...". furthermore, the tcag shows that lockdown mitigates diseasespread but it also has a negative impact on the economy, which will inform the decision makers that they will need to understand the economic trade-offs when implementing the lockdown policy. we can also analyze longer-distance causal pathways consisting of two or more causal/temporal edges. for example, our demo video shows that covid-19 causes or precedes (before) lockdown, and that lockdown causes or precedes economic-crisis. this helps us understand details about how covid causes economiccrisis. use case 2: trend and correlation analysis. we can inspect the event timeline for a node or an edge to perform a trend analysis and a correlation analysis, respectively. figure 5 shows screenshots of the event popularity timeseries between january and may 2020 for lockdown, economiccrisis and covid-19. first, the user can click on a single event to perform a trend analysis: the popularity of lockdown goes up continuously, indicating an upward trend in implementing lockdown policies in more geographic regions. the user can also click on a edge to perform a correlation analysis for a pair of events: when the user clicks on the edge {lockdown, causes, economiccrisis}, the ui shows a strong correlation between the two upward curves. for another edge "lockdown mitigates covid-19", the ui shows a negative correlation near the end: as lockdown rises, covid-19 slightly falls towards the end. use case 3: analyses targeted at geolocations. the event timeline visualization also allows the user to see the timeline for geolocations such as each u.s. state individually, instead of the aggregate for the entire u.s.. figure 6 is a screenshot showing the 10 timelines for lockdown for the top-10 most frequently mentioned u.s. states. the screenshot shows that the curves for california and new york go much higher than other states. this roughly matches the stricter lockdown policies implemented in the two states during this time period, compared with other states. such targeted analysis is made possible because our events have location and time arguments. we can also make the tcag only show events and relations for a specific state, if a user selects a state of interest in the ui. 9 related work extracting events. event extraction has been studied using feature-based approaches (huang and riloff, 2012; ji and grishman, 2008), or neural networks (chen et al., 2015; nguyen et al., 2016a; wadden et al., 2019; liu et al., 2020 ## conclusion and future work we present excavator, a machine reading system that automatically constructs a temporal and causal analysis graph for covid-19 by reading open-source text documents such as news and scientific publications. our next steps are to integrate modal dependency parsing @xcite for event factuality assessment, and cross-lingual transfer learning @xcite to make excavator applicable to more languages.