The GATE Crowdsourcing Plugin: Crowdsourcing Annotated Corpora Made Easy
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Corpus Annotation through Crowdsourcing: Towards Best Practice Guidelines
Crowdsourcing is an emerging collaborative approach that can be used for the acquisition of annotated corpora and a wide range of other linguistic resources. Although the use of this approach is intensifying in all its k…
Domain AdaptationNatural Language InferenceOpinion MiningQuestion Answering+1Microtask crowdsourcing for disease mention annotation in PubMed abstracts
Identifying concepts and relationships in biomedical text enables knowledge to be applied in computational analyses. Many biological natural language process (BioNLP) projects attempt to address this challenge, but the s…
BenchmarkingLearning from Measurements in Crowdsourcing Models: Inferring Ground Truth from Diverse Annotation Types
Annotated corpora enable supervised machine learning and data analysis. To reduce the cost of manual annotation, tasks are often assigned to internet workers whose judgments are reconciled by crowdsourcing models. We app…
Rethinking Crowdsourcing Annotation: Partial Annotation with Salient Labels for Multi-Label Image Classification
Annotated images are required for both supervised model training and evaluation in image classification. Manually annotating images is arduous and expensive, especially for multi-labeled images. A recent trend for conduc…
Active Learningimage-classificationImage ClassificationMulti-Label Image ClassificationCrowdsourcing Learning as Domain Adaptation: A Case Study on Named Entity Recognition
Crowdsourcing is regarded as one prospective solution for effective supervised learning, aiming to build large-scale annotated training data by crowd workers. Previous studies focus on reducing the influences from the no…
Domain Adaptationnamed-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)+2