Galke, Lukas Paul Achatius
Mai, Florian
Schelten, Alan
Brunsch, Dennis
Scherp, Ansgar
2017-12-06
<p>We conduct the first systematic comparison of automated semantic<br>
annotation based on either the full-text or only on the title metadata<br>
of documents. Apart from the prominent text classification baselines<br>
kNN and SVM, we also compare recent techniques of Learning<br>
to Rank and neural networks and revisit the traditional methods<br>
logistic regression, Rocchio, and Naive Bayes. Across three of our<br>
four datasets, the performance of the classifications using only titles<br>
reaches over 90% of the quality compared to the performance when<br>
using the full-text.</p>
https://doi.org/10.1145/3148011.3148039
oai:zenodo.org:1143955
eng
Zenodo
https://arxiv.org/abs/1705.05311
https://zenodo.org/communities/moving-h2020
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
K-CAP 2017, Ninth International Conference on Knowledge Capture, Austin, Texas, 04-06 December 2017
Multi-label classification
Document analysis
Semantic annotation
Using Titles vs. Full-text as Source for Automated Semantic Document Annotation
info:eu-repo/semantics/conferencePaper