presenter : chang, shih-jie authors : adnan yahya and ali salhi 2014. acm talip

Intelligent Database Systems Lab

Presenter : CHANG, SHIH-JIE

Authors : ADNAN YAHYA and ALI SALHI

2014. ACM TALIP.

Arabic Text Categorization Based on Arabic Wikipedia


OutlinesMotivationObjectivesMethodologyExperimentsConclusionsComments


Motivation

A challenge due to the correlation between certain subcategories and overlap between main categories.

EX:


Objectives• To solve this, we use algorithm and further adopt the two

approaches .


CATEGORIZATION CORPORA - Training Data

Related Tags Approach


Testing Data

10 categories with 40 documents in each category


Methodology - PREPROCESSING TECHNIQUES

Root Extraction (RE) Light Stemming (LS) Special Expressions Extraction


Methodology- CATEGORIZATION PROCESSCategorize the input text in two phases

Phase one: we categorize the text into one of the main categories.

Phase two:We further categorize the input text based on subcategories:


Methodology - Basic Categorization Algorithm (BCA)


Methodology - Percentage and Difference Categorization (PDC) Algorithm

has frequency 7 in the 300-word


Methodology - Percentage and Difference Categorization (PDC) Algorithm

The category with the highest sum of flag values is considered to be the best match for the input text.


Methodology – PDC Algorithm vs. BCA Algorithm


Methodology – Enhancing Main/Subcategories Grouping

(1) Overlapping Main Categories for Phase Two

Problem : The possible high correlation between subcategories of different main categories



(2) Replacing Main Categories by Groups of Related Categories


Methodology - Word Filtration Techniques within Categories


Methodology - The result of applying the three techniques


Modified PDC with N Scales Define a scaling of

1 0.5 0

1 0.5 00.250.75


Further Testing on the PDC AlgorithmTool Root ExtractionTool Light Stemming & Light10Tool Double WordsTool Expressions Extraction


Using Testing Data from the Reference Categories


Training Data Characteristics


COMPARISON WITH RELATED WORK


Using Testing Data from the Reference Categories


Conclusions– To use training and testing data from same source by

splitting the corpus into test and training components. This consistently gives better results.

– However, we believe that the second method (different source ) makes more sense, as the tests will

be more credible and indicative of performance in real-life environments.


Comments• Advantages

– To.• Applications

– Arabic Text Categorization .

presenter : chang, shih-jie authors : adnan yahya and ali salhi 2014. acm talip

Documents

arabic text categorization

input text

training components

mainsubcategories grouping2

certain subcategories

phase twoproblem

phases phase

method different source