Sentence examples for identification of text from inspiring English sources
This can be accomplished as part of a formative assessment of students' work, which includes access to similarity reports of software packages that support identification of text similarities (Davis 2009).
Transcripts were analysed for identification of text (meaning units) related to the processes, experiences and perceptions of partners on working together in implementing EmOC.
Transcripts from health workers and managers were analysed for identification of text (meaning units) related to informants' perspectives on provision of maternal care, health workers' experiences related to working environments, living conditions, dealing with staff welfare and staff management in general.
It may be useful to vary the confidence threshold by task; for example identification of texts for manual review requires high recall but precision is less important.
Though they differ in details and identification of parts, text scholars largely converge on attributing the chapters, outside of the eight assigned to Zhuangzi himself, to students of Zhuangzi, to primitivists who are associated with Yang Zhu (Yangists), and to other more eclectic and religious writers associated probably with the production of the other texts associated with Daoism.
Only the assignment training provided sufficient education to allow significant improvement in students' identification of plagiarized text, while the online training was comparable with student success of those who received no training.
For ease of reference, they appended content descriptions to the edges of tablets, and they adopted systematic shelving for quick identification of related texts.
Large US hospitals produce terabytes of data annually, but typically only about 8 GB of this is free text data (22 MB/day) [ 30], so that real time de-identification of free text data from a major hospital seems quite feasible with our software.
The de-identification of narrative text documents is often realized manually, and requires significant resources.
In this paper, we present a review of the state of the art in automated de-identification of clinical text.
