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  Using titles vs. full-text as source for automated semantic document annotation

Galke, L., Mai, F., Schelten, A., Brunch, D., & Scherp, A. (2017). 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.

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Item Permalink: https://hdl.handle.net/21.11116/0000-0009-F84A-D Version Permalink: https://hdl.handle.net/21.11116/0000-0009-F84E-9
Genre: Conference Paper

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 Creators:
Galke, Lukas1, Author           
Mai, Florian, Author
Schelten, Alan, Author
Brunch, Dennis, Author
Scherp, Ansgar, Author
Affiliations:
1ZBW – Leibniz Information Centre for Economics, Kiel, Germany, ou_persistent22              

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 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.

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Language(s): eng - English
 Dates: 2017
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1145/3148011.3148039
 Degree: -

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Title: K-CAP 2017: 9th International Conference on Knowledge Capture
Place of Event: Austin, TX, USA
Start-/End Date: 2017-12-04 - 2017-12-06

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Title: Proceedings of the 9th International Conference on Knowledge Capture (K-CAP 2017)
Source Genre: Proceedings
 Creator(s):
Corcho , Oscar, Editor
Janowicz, Krzysztof , Editor
Rizz, Giuseppe, Editor
Tiddi , Ilaria, Editor
Garijo , Daniel, Editor
Affiliations:
-
Publ. Info: New York : ACM
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: - Identifier: ISBN: 978-1-4503-5553-7