Papers with annotation models

3 papers
Learning from Measurements in Crowdsourcing Models: Inferring Ground Truth from Diverse Annotation Types (C18-1)

Copied to clipboard

Challenge: Annotated corpora are often assigned to internet workers whose judgments are reconciled by crowdsourcing models.
Approach: They propose a framework for learning from rich prior knowledge to combine annotations with different structures.
Outcome: The proposed model compares favorably with previous work and enables active sample selection to reduce annotation effort.
ARAIDA: Analogical Reasoning-Augmented Interactive Data Annotation (2024.acl-long)

Copied to clipboard

Challenge: Empirical studies demonstrate that Araida reduces human correction labor by 11.02% compared to vanilla interactive data annotation methods.
Approach: They propose an analogical reasoning-based approach that enhances automatic annotation accuracy in the interactive data annotation setting and reduces the need for human corrections.
Outcome: Empirical studies show that Araida reduces human correction labor by 11.02% compared to vanilla interactive data annotation methods.
Training a Swedish Constituency Parser on Six Incompatible Treebanks (2020.lrec-1)

Copied to clipboard

Challenge: Syntactic parsing is a widely used intermediate step in several natural language processing tasks.
Approach: They propose to use a function-tagged constituent treebank for Swedish which includes discontinuous constituents to improve the accuracy.
Outcome: The proposed parser can be trained on additional treebanks that use other annotation models.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations