Using titles vs. full-text as source for automated semantic document
Galke, L., Mai, F., Schelten, A., Brunch, D., & Scherp, A.
Using titles vs. full-text as source for automated semantic document annotation. In O. Corcho, K. Janowicz, G. Rizz, I. Tiddi, & D. Garijo (Eds.
), Proceedings of the 9th International Conference on Knowledge Capture (K-CAP 2017)
. New York: ACM.
We conduct the first systematic comparison of automated semantic
annotation based on either the full-text or only on the title metadata
of documents. Apart from the prominent text classification baselines
kNN and SVM, we also compare recent techniques of Learning
to Rank and neural networks and revisit the traditional methods
logistic regression, Rocchio, and Naive Bayes. Across three of our
four datasets, the performance of the classifications using only titles
reaches over 90% of the quality compared to the performance when
using the full-text.
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