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Carnegie Mellon Robotics Institute

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Abstract

Recent work has made much of using semantic knowledge, derived in particular from domain ontologies, for improving text learning tasks. Semantic knowledge is assumed to capture more in-depth knowledge of the text domain in comparison to more conventional statistics-based methods that can only rely on more surface vocabulary-specific characteristics of a data set. Therefore, using semantic knowledge instead of statistics-based methods will improve performance in text learning tasks significantly. We believe that this claim needs careful scrutiny and examine the validity of this assumption in this paper. We explore the usefulness of ontologies for a text classification task and the use of feature selection methods to extract terms that can function as candidate ontological concepts for building or extending ontologies. We point to a number of issues that arise when trying to use semantic knowledge for text classification,. One particularly troublesome issue is that semantic knowledge encoded in ontologies simply may not correspond to the concepts and terms significant for text classification.

Keywords

semantic web, semantic knowledge, ontologies, feature selection, text classification

Notes

Sponsor: AFRL

Grant ID: F30601-00-2-0592

Number of pages: 9

Text Reference

Anupriya Ankolekar, Young-Woo Seo, and Katia Sycara, "Investigating Semantic Knowledge for Text Learning," Proceedings of ACM SIGIR Workshop on Semantic Web, August, 2003.

BibTeX Reference

@inproceedings{Ankolekar_2003_4490,
   author = "Anupriya Ankolekar and Young-Woo Seo and Katia Sycara",
   title = "Investigating Semantic Knowledge for Text Learning",
   booktitle = "Proceedings of ACM SIGIR Workshop on Semantic Web",
   publisher = "ACM Press",
   month = "August",
   year = "2003",
}

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