activity
20202022
most citedezCoref: Towards Unifying Annotation Guidelines for Coreference Resolution

4 citations · 8 across the 4 of their papers we have counts for

collaborators

6 papers

cs.CL20224 cited

ezCoref: Towards Unifying Annotation Guidelines for Coreference Resolution

Ankita Gupta, Marzena Karpinska, Wenlong Zhao +5

Large-scale, high-quality corpora are critical for advancing research in coreference resolution. However, existing datasets vary in their definition of coreferences and have been c…

cs.CL20224 cited

Corpus-Guided Contrast Sets for Morphosyntactic Feature Detection in Low-Resource English Varieties

Tessa Masis, Anissa Neal, Lisa Green +1

The study of language variation examines how language varies between and within different groups of speakers, shedding light on how we use language to construct identities and how…

cs.CL2021

Corpus-Level Evaluation for Event QA: The IndiaPoliceEvents Corpus Covering the 2002 Gujarat Violence

Andrew Halterman, Katherine A. Keith, Sheikh Muhammad Sarwar +1

Automated event extraction in social science applications often requires corpus-level evaluations: for example, aggregating text predictions across metadata and unbiased estimates…

cs.CL2020

Analyzing Gender Bias within Narrative Tropes

Dhruvil Gala, Mohammad Omar Khursheed, Hannah Lerner +2

Popular media reflects and reinforces societal biases through the use of tropes, which are narrative elements, such as archetypal characters and plot arcs, that occur frequently ac…

cs.CL2020

Uncertainty over Uncertainty: Investigating the Assumptions, Annotations, and Text Measurements of Economic Policy Uncertainty

Katherine A. Keith, Christoph Teichmann, Brendan O'Connor +1

Methods and applications are inextricably linked in science, and in particular in the domain of text-as-data. In this paper, we examine one such text-as-data application, an establ…

cs.CL2020

Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates

Katherine A. Keith, David Jensen, Brendan O'Connor

Many applications of computational social science aim to infer causal conclusions from non-experimental data. Such observational data often contains confounders, variables that inf…