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

Intelligent Database Systems Lab

OutlinesMotivationObjectivesMethodologyExperimentsConclusionsComments

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Motivation

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

tics, cognitive science and artificial intelligence.

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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.

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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)

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

Methodology

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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

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• 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

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• 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-

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Methodology• Semantic relatedness computing system

architecture– Semantic relatedness computing

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Methodology• Evaluating semantic relatedness measuresComparison with human judgments

Pearson product-moment correlation coefficient

Spearman rank order correlation coefficient

Datasets

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Experiments• Our semantic relatedness computing system modules using

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

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Experiments• Basic system

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Experiments• First module: simple patterns

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Experiments• Second module: Wikipedia pages

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Experiments• Third module: enrichment using categories

neighbors in WCG

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Experiments• Forth module: Categories enrichment using WCG

and redirects

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

• Solving word choice problems

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

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

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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