Letting the Data Speak: Extracting Keywords from Crowdsourced Collections with AI
Identifying and assigning keywords at scale is a technical, practical, and ethical challenge for crowdsourced collections. This article reports the findings of the "Extracting Keywords from Crowdsourced Collections" project, which used the Their Finest Hour Online Archive, a crowdsourced Second World War digital collection hosted by the University of Oxford, as a case study. The project evaluated three Natural Language Processing approaches to automate keyword extraction: Named Entity Recognition, Keyword Extraction, and Topic Modelling. It tested these approaches across a range of artificial
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- PossiblePossibly related (embedding) · 47%JohnSnowLabs/spark-nlp →
- PossiblePossibly related (embedding) · 45%0x11c11e/awesome-ai-research-tools →
- LinkedLinked via arxiv author · 85%Miguel Arana-Catania →
“Letting the Data Speak: Extracting Keywords from Crowdsourced Collections with AI”
- LinkedLinked via arxiv author · 85%Catherine Conisbee →
“Letting the Data Speak: Extracting Keywords from Crowdsourced Collections with AI”
- LinkedLinked via arxiv author · 85%Matthew Kidd →
“Letting the Data Speak: Extracting Keywords from Crowdsourced Collections with AI”