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Found 8 papers, 6 papers with code
SemEval-2021 Task 12: Learning with Disagreements
Disagreement between coders is ubiquitous in virtually all datasets annotated with human judgements in both natural language processing and computer vision.
Beyond Optimizing for Clicks: Incorporating Editorial Values in News Recommendation
In our first study we explore how our news recommender steers reading behavior in the context of editorial values such as serendipity, dynamism, diversity, and coverage.
A Crowdsourced Frame Disambiguation Corpus with Ambiguity
We present a resource for the task of FrameNet semantic frame disambiguation of over 5, 000 word-sentence pairs from the Wikipedia corpus.
Crowdsourcing Semantic Label Propagation in Relation Classification
Distant supervision is a popular method for performing relation extraction from text that is known to produce noisy labels.
CrowdTruth 2.0: Quality Metrics for Crowdsourcing with Disagreement
However, in many domains, there is ambiguity in the data, as well as a multitude of perspectives of the information examples.
Human-Computer Interaction Social and Information Networks
Capturing Ambiguity in Crowdsourcing Frame Disambiguation
FrameNet is a computational linguistics resource composed of semantic frames, high-level concepts that represent the meanings of words.
False Positive and Cross-relation Signals in Distant Supervision Data
Distant supervision (DS) is a well-established method for relation extraction from text, based on the assumption that when a knowledge-base contains a relation between a term pair, then sentences that contain that pair are likely to express the relation.
Crowdsourcing Ground Truth for Medical Relation Extraction
Cognitive computing systems require human labeled data for evaluation, and often for training.
