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Found 8 papers, 3 papers with code
Concept Wikification for COVID-19
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.
Taxonomy Builder: a Data-driven and User-centric Tool for Streamlining Taxonomy Construction
An existing domain taxonomy for normalizing content is often assumed when discussing approaches to information extraction, yet often in real-world scenarios there is none. When one does exist, as the information needs shift, it must be continually extended.
Annotating Temporal Dependency Graphs via Crowdsourcing
We present the construction of a corpus of 500 Wikinews articles annotated with temporal dependency graphs (TDGs) that can be used to train systems to understand temporal relations in text.
ZS4IE: A toolkit for Zero-Shot Information Extraction with simple Verbalizations
The current workflow for Information Extraction (IE) analysts involves the definition of the entities/relations of interest and a training corpus with annotated examples.
Factuality Assessment as Modal Dependency Parsing
In this paper, we frame factuality assessment as a modal dependency parsing task that identifies the events and their sources, formally known as conceivers, and then determine the level of certainty that the sources are asserting with respect to the events.
ExcavatorCovid: Extracting Events and Relations from Text Corpora for Temporal and Causal Analysis for COVID-19
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.
Towards Machine Reading for Interventions from Humanitarian-Assistance Program Literature
Solving long-lasting problems such as food insecurity requires a comprehensive understanding of interventions applied by governments and international humanitarian assistance organizations, and their results and consequences.
Rapid Customization for Event Extraction
We present a system for rapidly customizing event extraction capability to find new event types and their arguments.
