Text Classification Using Semantic Networks
Date Issued
2011-03
Author(s)
Jovanovik, Milos
Abstract
In the age of information overflow, we face with the
challenge of categorizing the digital information we come
across on a daily basis, in order to apply different operations
and priorities to different types of information and to manage
to use it in a more efficient manner. This issue introduces the
challenge of automatic text classification. The problem of text
classification can be defined as assigning one or more
categories to a certain text, based on its contents. There are
many different approaches for solving this problem: one of
the solutions is the use of latent semantic analysis (LSA),
statistical text analysis, etc.
This paper introduces an algorithm for text classification
with the use of semantic networks. In this paper we present a
method for knowledge representation needed for this type of
text analysis. We also show how to create this knowledge
representation and how to use it to assign one or more
categories to a given text.
challenge of categorizing the digital information we come
across on a daily basis, in order to apply different operations
and priorities to different types of information and to manage
to use it in a more efficient manner. This issue introduces the
challenge of automatic text classification. The problem of text
classification can be defined as assigning one or more
categories to a certain text, based on its contents. There are
many different approaches for solving this problem: one of
the solutions is the use of latent semantic analysis (LSA),
statistical text analysis, etc.
This paper introduces an algorithm for text classification
with the use of semantic networks. In this paper we present a
method for knowledge representation needed for this type of
text analysis. We also show how to create this knowledge
representation and how to use it to assign one or more
categories to a given text.
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