Bifurcation of Semi-Automated Subject Indexing Services

Abstract

Semi-automated subject indexing methods use attributes from metadata descriptions as training data. A survey to shape inclusion of metadata attributes that align a machine learning model within a contextual linguistic domain generated the initial genre targets for experimentation. The second part of this study then tested the genre attributes from the survey. These bifurcations (or branching points) served as the basis for machine learning model development and evaluation. The machine learning models in semi-automated indexing systems are the drivers of the automated subject outputs. The initial results of this multipart experiment indicate that measures of the mean precision and recall (the F1 metric) improved for several—but not all—types of genres that were of interest to knowledge workers.