A similarity measure based on semantic and linguistic information

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Digital Enterprise Research Institute www.deri.ie

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A Similarity Measure Based on Semantic and Linguistic Information

Nitish AggarwalDERI, NUI Galway

[email protected]

Wednesday,15th June, 2011DERI, Reading Group


Based On:

“A Feature and Information Theoretic Framework for Semantic Similarity and Relatedness”

Authors: Giuseppe Pirro and JeoromeEuzenat

Published: International Semantic Web Conference, 2010

“SyMSS: A syntax-based measure for short-text semantic similarity ”

Author: J. Oliva, J. Serrano, M. Castillo, and Ángel Iglesias

Published: Journal Data & Knowledge Engineering, Volume 70 Issue 4 April,2011

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Overview

Introduction

Classical Approaches

Ontology-based Similarity

Set of relations

Information Content

SyMSS (Syntax-based) Deep Parsing

Influence of adjectives and adverbs

Conclusion

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Introduction & Motivation

Short-text Similarity Lack of Semantics and Linguistics

Applications Semantic Annotation Semantic Search Information Retrieval and Extraction

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Classical Approaches

String Similarity Levenshtein distance, Dice Coefficient

Corpus-based ESA, Google distance,Vector-Space Model

Ontology-based Path distance, Information content

Syntax Similarity Word-order, Part of Speech

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First Paper:







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Ontology-based - Overview

Features Whole set of semantic relations defined in an ontology

Resnik’s Information Content IC(c) = -log p(c)

Intrinsic Information Content Overcome the analysis of large corpora

Extended Information Content Map feature-based model to information theoretic

domain

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Ontology-based - Why whole set?

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Eyes Ears

Relation: Part of


Ontology-based - model

Tversky’s feature-based similarity model common features of two concepts ~ similarity Extra feature ~ 1/similarity .

Ratio-base formulation of Tverky’s model

.

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Ontology-based - Mapping

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Mapping between feature-based and information theoretic similarity models

1. MSCA: Most Specific Common Abstraction


Ontology-based - Example

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T1: Car

T2: Bicycle

Example of Concept Feature


Ontology-based - Example

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T1: Car

T2: Bicycle

Example of Concept Feature


Ontology-based - Framework

Intrinsic information content(iIC)

.

where sub(c) is number of sub-concept of given concept c.

Extended information content(eIC) where EIC(c) is relatedness coefficient using all kind of relations

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Digital Enterprise Research Institute www.deri.ie DataSet: 65 human evaluated pairs

Correlation values:

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Ontology-based – Evaluation of Similarity


Ontology-based – Evaluation of Relatedness

DataSet : Wordnet 353

Correlation value:

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Ontology-based - Summary

Intrinsic similarity measure Ontology-based similarity Outperforms corpus measures

Limitation No short-text Model-based

– E,g, only concepts in the ontology are considered (e.g. car accident)


Second paper (SyMSS)







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SyMSS - Overview

SyMSS = “syntax-based similarity for short-term text”

Syntactic Information Not only word order Deep Parsing Parts of speech

Semantic Information Wordnet similarity Different ontology-based similarity

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SyMSS - Semantic Information

Path-base measure Shortest path Hirst and st. Onge (HSO)

Information Content Resnik measure Jiang and Corath measure Lin measure

Gloss-base measure Gloss Overlap and Gloss vector

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SyMSS - Syntactic Information

Parse tree phrases Head of phrases

Head similarity Head of phrases which have same syntactic function

Penalization factor Non shared phrases

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SyMSS - Model

My brother has a dog with four legs

My brother has four legs

Sim(Has,Has) = 1

Sim(brother,brother) = 1Sim(dog,leg) = 0.1414

PF = 0.03


SyMSS - Evaluation

DataSet: 30 pairs out of 65 human evaluated pairs

Correlation values:

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SyMSS - Effect of adverb and adjective

Sentence1: ”I have a big dog”

Sentence2: ”I have a little dog”

8.68% gain in SyMSS with HSO

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SyMSS - Summary

Syntax-based similarity considers… Nouns and verbs Influence of adjectives and adverbs

Limitation Depend on parsed structure

– E.g. not grammatically correct Depend on word similarity


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Conclusion

No established method for short text Parsing of phrases is difficult

Concept similarity depend on model Weak model

– E.g. xebr: Extraordinary Income and xebr: Other Operating Income ->

Pathlength = 0.2 and Expert = 0.8

Need a syntactic similarity for concepts tag (word or phrase)

A similarity measure based on semantic and linguistic information

Education

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