Using Titles vs. Full-text as Source for Automated Semantic Document Annotation

Lukas Galke, Florian Mai, Alan Schelten, Dennis Brunsch, Ansgar Scherp

Abstract: 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.