Abstract
Current representation schemes for automatic text classification treat documents as syntactically unstructured collections of words or ‘concepts’. Past attempts to encode syntactic structure have treated part-of-speech information as another word-like feature, but have been shown to be less effective than non-structural approaches. Here, we investigate three methods to augment semantic modelling with syntactic structure, which encode the structure across all features of the document vector while preserving text semantics. We present classification results for these methods versus the Bag-of-Concepts semantic modelling representation to determine which method best improves classification scores.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Kehagias, A., et al.: A comparison of word- and sense-based text categorization using several classification algorithms. Journal of Intelligent Information Systems 21(3), 227–247 (2003)
Moschitti, A., Basili, R.: Complex Linguistic Features for Text Classification: A Comprehensive Study. In: McDonald, S., Tait, J.I. (eds.) ECIR 2004. LNCS, vol. 2997, pp. 181–196. Springer, Heidelberg (2004)
Sahlgren, M., Cöster, R.: Using Bag-of-Concepts to Improve the Performance of Support Vector Machines in Text Categorization. In: Proceedings of the 20th International Conference on Computational Linguistics, pp. 487–493 (2004)
Sahlgren, M.: An Introduction to Random Indexing. In: Methods and Applications of Semantic Indexing Workshop at the 7th International Conference on Terminology and Knowledge Engineering (2005)
Plate, T.A.: Holographic Reduced Representation: Distributed representation for cognitive structures. CSLI Publications, Stanford (2003)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Fishbein, J.M., Eliasmith, C. (2008). Methods for Augmenting Semantic Models with Structural Information for Text Classification. In: Macdonald, C., Ounis, I., Plachouras, V., Ruthven, I., White, R.W. (eds) Advances in Information Retrieval. ECIR 2008. Lecture Notes in Computer Science, vol 4956. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78646-7_58
Download citation
DOI: https://doi.org/10.1007/978-3-540-78646-7_58
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78645-0
Online ISBN: 978-3-540-78646-7
eBook Packages: Computer ScienceComputer Science (R0)