Intelligent Database Systems Lab Presenter : YAN-SHOU SIE Authors Mohamed Ali Hadj Taieb *, Mohamed...

Intelligent Database Systems Lab

Presenter : YAN-SHOU SIE

Authors Mohamed Ali Hadj Taieb *, Mohamed Ben Aouicha,

Abdelmajid Ben Hamadou

2013. KBS

Computing semantic relatedness using Wikipedia features


Outlines

MotivationObjectivesMethodologyExperimentsConclusionsComments


Motivation

• Measuring semantic relatedness is a critical task in many domains such as psychology, biology, linguis-

tics, cognitive science and artificial intelligence.


Objectives

• We propose a novel system for computing semantic relatedness between words. Recent approaches have exploited Wikipedia as a huge semantic resource that showed good performances.


Methodology• Our semantic relatedness computing system– Filtering Wikipedia category graph– pre-processing• Filtering article content• Porter stemming• Weighting article stems• Providing a Category Semantic Depiction (CSD)


• Different steps performed to generate the Category Semantic DepictionFiltering Wikipedia category graph

Methodology


Methodology• Filtering Wikipedia category graph– First : clean meta-categories

» We remove all those nodes whose labels contain any of the following strings : Wikipedia, wikiproject, lists, mediawiki,template, user, portal, categories, articles, pages, stub and album

– Second : remove orphan nodes and we keep only the category Contents as root» maximum depth 291 to 221


• pre-processing– Filtering article content

» Remove html tags,infobox, language translation, hyperlinks. . .

– Porter stemming» filtered a stop list to eliminate words which do not have any

contribution.

– Weighting article stems

– Providing a Category Semantic Depiction (CSD)

Methodology


• Semantic relatedness computing system architecture– Extraction categories algorithm• WordNet:• resolve the disambiguation pages problem:

– Setp1 : extracting all outLinks– Setp2 : find links containing disambiguation tag in parenthesis– Setp3 : extract categories to the two first links – Final : take the categories of the article assigned to the first link existing in the ordered set

Methodology-


Methodology• Semantic relatedness computing system

architecture– Semantic relatedness computing


Methodology• Evaluating semantic relatedness measuresComparison with human judgments

Pearson product-moment correlation coefficient

Spearman rank order correlation coefficient

Datasets


Experiments• Our semantic relatedness computing system modules using

Wikipedia features– Basic system– First module– Second module– Third module– Forth module


Experiments• Basic system


Experiments• First module: simple patterns


Experiments• Second module: Wikipedia pages


Experiments• Third module: enrichment using categories

neighbors in WCG


Experiments• Forth module: Categories enrichment using WCG

and redirects


Experiments• Application of the SR measure on other datasets– Datasets RG-65 and MC-30– The verbal dataset YP-130

• Solving word choice problems


Conclusions• Our result system shows a good performance and outperforms sometimes

ESA (Explicit Semantic Analysis) and TSA (Temporal Semantic Analysis) approaches


Comments• Advantages

Able to use wiki to get a lot of semantic relationship information, semantic relations for many measurements related work of great help.

• Applications– cognitive science– artificial intelligence

Intelligent Database Systems Lab Presenter : YAN-SHOU SIE Authors Mohamed Ali Hadj Taieb *, Mohamed...

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