Investigating Semantic Knowledge for Text Learning
نویسندگان
چکیده
ABSTRACT Re ent work has made mu h of using semanti knowledge, derived in parti ular from domain ontologies, for improving text learning tasks. Semanti knowledge is assumed to apture more in-depth knowledge of the text domain in omparison with onventional statisti s-based methods that an only rely on more surfa e vo abulary-spe i hara teristi s of a data set. Therefore, using semanti knowledge instead of statisti s-based methods should improve performan e in text learning tasks signi antly. We believe that this laim needs areful s rutiny and examine the validity of this assumption in this paper. We explore the usefulness of ontologies for a text lassi ation task and the use of feature sele tion methods to extra t terms that an fun tion as andidate ontologi al on epts for building or extending ontologies. We point to a number of issues that arise when trying to use semanti knowledge for text lassi ation. One parti ularly troublesome issue is that semanti knowledge en oded in ontologies simply may not orrespond to the on epts and terms signi ant for text lassi ation.
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