Challenge: SciSight is a system for exploratory search of COVID-19 literature . it explores associations between biomedical facets extracted from papers .
Approach: They propose a system for exploratory search of COVID-19 literature that integrates two key capabilities: first, exploring associations between biomedical facets automatically extracted from papers; second, combining textual and network information to search and visualize groups of researchers and their ties.
Outcome: The proposed system has served over 15K users with over 42K page views and 13% returns.

Similar Papers

CovRelex: A COVID-19 Retrieval System with Relation Extraction (2021.eacl-demos)

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Challenge: Existing challenges to making the system more practical include dealing with newly created and unknown data, and solving the performance gap when utilizing present data.
Approach: They propose a scientific paper retrieval system targeting entities and relations via relation extraction on COVID-19 scientific papers.
Outcome: The proposed system can be accessed via https://www.jaist.ac.jp/is/labs/nguyen-lab/systems/covrelex/.
Fighting the COVID-19 Infodemic: Modeling the Perspective of Journalists, Fact-Checkers, Social Media Platforms, Policy Makers, and the Society (2021.findings-emnlp)

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Challenge: a dataset of 16K manually annotated tweets is used to analyze disinformation . the democratic nature of social media has raised questions about the quality and the factuality of the information that is shared on these platforms.
Approach: They use a dataset of manually annotated tweets to analyze COVID-19 disinformation . they show that tweets contain fake cures, rumors, conspiracy theories and xenophobia .
Outcome: The proposed dataset shows that it is useful in monolingual vs. multilingual settings.
Extracting a Knowledge Base of Mechanisms from COVID-19 Papers (2021.naacl-main)

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Challenge: COVID-19 has spawned a diverse body of scientific literature that is challenging to navigate . researchers are using automated tools to help find useful knowledge .
Approach: They develop a schema to extract mechanism relations from scientific papers . their search engine, dataset and code are publicly available .
Outcome: The proposed schema outperforms PubMed search in clinical trials.
Exploration and Discovery of the COVID-19 Literature through Semantic Visualization (2021.naacl-srw)

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Challenge: Existing semantic visualization methods are limited in finding connections between corpora targeting a specific topic.
Approach: They propose to use semantic visualization to explore large datasets of complex networks by exploiting the semantics of the relations in them.
Outcome: The proposed method can enable exploration and discovery over large datasets of complex networks by exploiting the semantics of the relations in them.
COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation (2021.naacl-demos)

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Challenge: a new framework to digest relevant biomedical knowledge is needed to combat COVID-19 . quantity of research results is a bottleneck, and false information promoted in publications .
Approach: a team of researchers has developed a framework to extract multimedia knowledge elements from scientific literature to combat COVID-19.
Outcome: a new framework extracts fine-grained multimedia knowledge elements from scientific literature . it provides detailed contextual sentences, subfigures, and knowledge subgraphs as evidence . the framework is based on a case study of drug repurposing .
CovRelex-SE: Adding Semantic Information for Relation Search via Sequence Embedding (2023.eacl-demo)

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Challenge: COVID-19 has affected all aspects of human life, causing problems related to acronyms, synonyms, and rare keywords.
Approach: They propose a hybrid relation retrieval system based on embeddings to provide high-quality search results.
Outcome: The proposed system can be accessed through the following URL: http://www.jaist.ac.jp/is/labs/nguyen-lab/systems/covrelex-se/.
ExcavatorCovid: Extracting Events and Relations from Text Corpora for Temporal and Causal Analysis for COVID-19 (2021.emnlp-demo)

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Challenge: a new machine reading system ingests open-source text documents to analyze COVID-19 events . the system extracts COVId-19 related events and relations between them .
Approach: They propose a machine reading system that ingests open-source text documents and extracts COVID-19 related events and relations between them.
Outcome: The proposed system extracts COVID-19 related events and relations from open-source text . it will help government agencies alleviate the information overload and respond to COVId-19 .
CoVERT: A Corpus of Fact-checked Biomedical COVID-19 Tweets (2022.lrec-1)

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Challenge: Existing fact-checking resources cover COVID-19 related information in news, but there is no dataset providing fact- checked COVId-19 related tweets with detailed annotations for biomedical entities, relations and relevant evidence.
Approach: They propose a fact-checked corpus of tweets with annotations for biomedical entities, relations and relevant evidence for COVID-19 related tweets.
Outcome: The proposed dataset provides fact-checked COVID-19 related tweets with detailed annotations for biomedical entities, relations and relevant evidence.
Extracting a Knowledge Base of COVID-19 Events from Social Media (2022.coling-1)

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Challenge: a flood of COVID-19 related information has appeared on social media since December 2019 . this includes reports on public figures who have tested positive/negative for the virus .
Approach: They construct a corpus of 10,000 tweets with annotated public reports of five COVID-19 events, using slot-filling questions to fill in slots.
Outcome: The proposed method can be quickly applied to develop knowledge bases for new domains in response to emerging crises, including natural disasters or future disease outbreaks.
Knowledge Navigator: LLM-guided Browsing Framework for Exploratory Search in Scientific Literature (2024.findings-emnlp)

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Challenge: Knowledge Navigator organizes retrieved documents into a navigable, two-level hierarchy of named and descriptive topics and subtopics.
Approach: They propose to organize retrieved scientific documents into a navigable, two-level hierarchy of named and descriptive topics and subtopics.
Outcome: The proposed system provides an overall view of the research themes in a domain while also enabling iterative search and deeper knowledge discovery within specific subtopics.

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