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Hidden Markov Model for Text Classification using
LC Classification. Edition No. 1
VDM Publishing House, Sep 2008, Pages: 180
Text classification (TC) is the task of automatically
categorizing text into pre-set categories by
analyzing their contents. In this monograph, a
framework of a new TC model based on hidden Markov
model is proposed, and its implementation is
demonstrated in an application of library and
information science. Two primary objectives are:
First, the development of a new TC model based on
hidden Markov model (HMM) proposed as a new framework
for TC task. HMM has been applied to a wide range of
applications in text processing such as text
segmentation and event tracking, information
retrieval, and information extraction. Few, however,
have applied HMM to TC. Second, the application of
the Library of Congress Classification (LCC) as a
classification scheme for automatically organizing
digital resources. A general prototype for an
HMM-based TC model is proposed and implemented, so as
to classify a collection of dissertation abstracts
from the ProQuest Digital Dissertations database into
LCC. The proposed model is compared to a Naïve
Bayesian model, which has been extensively used in TC
applications. Lastly, current TC challenges and
issues are discussed.
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