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Found 5 papers, 3 papers with code
Describing and Organizing Semantic Web and Machine Learning Systems in the SWeMLS-KG
In line with the general trend in artificial intelligence research to create intelligent systems that combine learning and symbolic components, a new sub-area has emerged that focuses on combining machine learning (ML) components with techniques developed by the Semantic Web (SW) community - Semantic Web Machine Learning (SWeML for short).
Effective Crowd-Annotation of Participants, Interventions, and Outcomes in the Text of Clinical Trial Reports
Obtaining such a corpus from crowdworkers, however, has been shown to be ineffective since (i) workers usually lack domain-specific expertise to conduct the task with sufficient quality, and (ii) the standard approach of annotating entire abstracts of trial reports as one task-instance (i. e. HIT) leads to an uneven distribution in task effort.
DEXA: Supporting Non-Expert Annotators with Dynamic Examples from Experts
of 0. 68 to experts in DEXA vs. 0. 40 in CONTROL); (ii) already three per majority voting aggregated annotations of the DEXA approach reach substantial agreements to experts of 0. 78/0. 75/0. 69 for P/I/O (in CONTROL 0. 73/0. 58/0. 46).
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.
Leveraging the Wisdom of the Crowds for the Acquisition of Multilingual Language Resources
We describe the goals and structure of the game, the underlying application framework, the sentiment lexicons gathered through crowdsourcing, as well as a novel approach to automatically extend the lexicons by means of a bootstrapping process.
