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a new probabilistic model for
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text categorization
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. Compared to previous models
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C00-1066 |
Jungyun Seo Abstract The goal of
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text categorization
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is to classify documents into
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</title> Makoto IWAYA M A Abstract
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Text categorization
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is the classification of documents
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be used in areas where low-cost
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text categorization
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is needed . It also can be used
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A94-1027 |
major probabilistic models for
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text categorization
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. Originally , these models have
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A94-1027 |
trieval , but the adaptation to
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text categorization
|
is straightforward . In a model
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A94-1027 |
<title> A Probabilistic Model for
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Text Categorization
|
: Based on a Single Random Variable
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A92-1025 |
Joining Statistics with NLP for
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Text Categorization
|
</title> Paul S Jacobs no title
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A92-1025 |
lexical acquisition . 1 Introduction
|
Text categorization
|
is an excellent application domain
|
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information . Carnegie Group 's
|
Text Categorization
|
Shell ( TCS ) -LSB- 1 -RSB- is
|
A92-1024 |
component of Carnegie Group 's
|
Text Categorization
|
Shell is a powerful pattern matcher
|
A94-1027 |
Brief Survey of Probabilistic
|
Text Categorization
|
In this section , we will briefly
|
A00-1038 |
Related Work The Carnegie Group 's
|
Text Categorization
|
Shell ( TCS ) ( Hayes , 1992
|
A94-1027 |
has better characteristics for
|
text categorization
|
compared to the previous models
|
A94-1027 |
. In a model of probabilistic
|
text categorization
|
, P ( cid ) = " the probability
|
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for information retrieval and
|
text categorization
|
( Jacobs , 1992 ; Cutting etal.
|
C00-1066 |
considerably increased . As a result ,
|
text categorization
|
has become one of the key techniques
|
A97-1018 |
such as information retrieval ,
|
text categorization
|
, text proofreading , OCR , speech
|
A94-1027 |
a new probabilistic model for
|
text categorization
|
, and compare it to the previous
|
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combined directly . <title> Automatic
|
Text Categorization
|
by Unsupervised Learning </title>
|