With the surging of smartphone sensing, wireless networking, and mobile social networking
techniques, Mobile Crowd Sensing and Computing (MCSC) has become a promising …

We present Ditto, a novel entity matching system based on pre-trained Transformer-based
language models. We fine-tune and cast EM as a sequence-pair classification problem to …

Crowdsourcing has emerged as a novel problem-solving paradigm, which facilitates
addressing problems that are hard for computers, eg, entity resolution and sentiment …

Entity resolution is central to data integration and data cleaning. Algorithmic approaches
have been improving in quality, but remain far from perfect. Crowdsourcing platforms offer a …

Many organizations are now starting to introduce crowdsourcing as a new model of
business to outsource tasks, which are traditionally performed by a small group of people, to …

Any important data management and analytics tasks cannot be completely addressed by
automated processes. These tasks, such as entity resolution, sentiment analysis, and image …

We tackle the problem of entity linking for large collections of online pages; Our system,
ZenCrowd, identifies entities from natural language text using state of the art techniques and …

This survey provides a comprehensive overview of the landscape of crowdsourcing
research, targeted at the machine learning community. We begin with an overview of the …

Recent approaches to crowdsourcing entity matching (EM) are limited in that they
crowdsource only parts of the EM workflow, requiring a developer to execute the remaining …

Crowdsourcing is widely accepted as a means for resolving tasks that machines are not
good at. Unfortunately, Crowdsourcing may yield relatively low-quality results if there is no …