Gomez, J.; Allen, K.; Matney, M.; Awopetu, T.; Shafer, S.: Experimenting with a machine generated annotations pipeline (2020)
0.04
0.038870275 = product of:
0.07774055 = sum of:
0.07774055 = product of:
0.1554811 = sum of:
0.1554811 = weight(_text_:tagging in 657) [ClassicSimilarity], result of:
0.1554811 = score(doc=657,freq=2.0), product of:
0.2979515 = queryWeight, product of:
5.9038734 = idf(docFreq=327, maxDocs=44218)
0.05046712 = queryNorm
0.5218336 = fieldWeight in 657, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
5.9038734 = idf(docFreq=327, maxDocs=44218)
0.0625 = fieldNorm(doc=657)
0.5 = coord(1/2)
0.5 = coord(1/2)
- Abstract
- The UCLA Library reorganized its software developers into focused subteams with one, the Labs Team, dedicated to conducting experiments. In this article we describe our first attempt at conducting a software development experiment, in which we attempted to improve our digital library's search results with metadata from cloud-based image tagging services. We explore the findings and discuss the lessons learned from our first attempt at running an experiment.