Naive Rule Induction For Text Classification
Based On Key-phrases
Author(s)
N. N. Karanikolas & C. Skourlas
Abstract
In this paper we focus on the induction of naive rules for classifying text
documents. An algorithm is briefly described for the creation of key-phrases
from a given set of documents and these key-phrases are organized and used as
features for the automatic classification of new documents. An Authority list of
key-phrases is specified by the algorithm containing key-phrases that occur
frequently within the documents of only one or a few classes in the training set.
In this framework, this last property permitted the creation of naive rules that
measure the similarity of new documents with the existing classes.
Keywords: text data mining, text classification, instance based learning, rule
induction.
1 Introduction
Key-phrases or search terms could be defined as sequences of adjacent words
within a text window (e.g. five successive words of the text / a sentence) forming
a meaningful, descriptive phrase related to the content of the text document.
Such terms can be used as features for classifying (text) documents. Since, not
every key-phrase is appropriate for discriminating between documents, we have
to examine and apply methods for selecting the appropriate ones. Hence, a
prerequisite for such a classification method is the use and maintenance of a list
of key-phrases, the so-called \“Authority List” Karanikolas and Skourlas [4]. An
interesting problem is related to the reduction of the search space that is needed
for the extraction of candidate key-phrases.
In Classification learning, a learning scheme takes a set of classified
examples from which it is expected to learn a way of classifying unseen
Keywords
text data mining, text classification, instance based learning, rule
induction.
Related Book
Other papers in this volume
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