Papers by Kristin Wright-Bettner

5 papers
LiDARR: Linking Document AMRs with Referents Resolvers (2025.acl-demo)

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Challenge: Abstract Meaning Representation (AMR) is a formalism for semantic representation of natural language text.
Approach: They propose a web tool for semantic annotation at the document level using Abstract Meaning Representation (AMR) it integrates an AMR-to-surface alignment model and a coreference resolution model into the tool .
Outcome: The proposed tool simplifies the creation of knowledge graphs from natural language documents . it integrates an AMR-to-surface alignment model and coreference resolution model .
Spatial AMR: Expanded Spatial Annotation in the Context of a Grounded Minecraft Corpus (2020.lrec-1)

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Challenge: Existing annotation tools for spatial relations capture fine-grained semantics and pragmatics derived from spatial information.
Approach: They propose an extension to the Abstract Meaning Representation annotation schema that captures fine-grained spatial information in grounded corpora.
Outcome: The proposed tool can handle fine-grained spatial relationships grounded in quantized space.
Linear Cross-document Event Coreference Resolution with X-AMR (2024.lrec-main)

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Challenge: Event Coreference Resolution (ECR) is expensive both for automated systems and manual annotations.
Approach: They propose a graphical representation of events anchored around individual mentions using a cross-document version of Abstract Meaning Representation.
Outcome: The proposed model is anchored around individual mentions using a cross-document version of Abstract Meaning Representation.
Cross-document coreference: An approach to capturing coreference without context (D19-62)

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Challenge: a cross-document coreference annotation schema was developed to extract timelines in the clinical domain.
Approach: They propose a cross-document coreference annotation schema that is governed by schematic rules to create meaningful and consistent cross- document relations.
Outcome: The proposed approach produces an agreement score of 93.77% for identical relations between the two sets of notes.
CAMRA: Copilot for AMR Annotation (2023.emnlp-demo)

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Challenge: Abstract Meaning Representation (AMR) is a formalism for deep lexical semantic representation.
Approach: They introduce a web-based tool for constructing AMR from natural language text . CAMRA incorporates AMR parser models as coding co-pilots .
Outcome: The proposed tool is based on the prototyping of existing AMR editors and integrates Propbank roleset lookup as an autocomplete feature.

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