A comparative study of conversion aided methods for WordNet sentence textual similarity

Muhidin Mohamed; Mourad Oussalah

A comparative study of conversion aided methods for WordNet sentence textual similarity

College of Engineering and Physical Sciences

Research output: Unpublished contribution to conference › Unpublished Conference Paper › peer-review

Abstract

In this paper, we present a comparison of three methods for taxonomic-based sentence semantic relatedness, aided with word parts of speech (PoS) conversion. We use WordNet ontology for determining word level semantic
similarity while augmenting WordNet with two other lexicographical databases; namely Categorial Variation
Database (CatVar) and Morphosemantic Database in assisting the word category conversion. Using a human
annotated benchmark data set, all the three approaches achieved a high positive correlation reaching up to (r =
0.881647) with comparison to human ratings and two other baselines evaluated on the same benchmark data set.

Original language	English
Pages	37-42
Publication status	Published - 23 Aug 2014

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This work is licensed under a Creative Commons Attribution 4.0 International License. Page numbers and proceedings footer are added by the organizers. License details: http://creativecommons.org/licenses/by/4.0/

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A Comparative Study of Conversion Aided Methods for WordNet Sentence Textual SimilarityFinal published version, 476 KBLicence: CC BY 3.0

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title = "A comparative study of conversion aided methods for WordNet sentence textual similarity",

abstract = "In this paper, we present a comparison of three methods for taxonomic-based sentence semantic relatedness, aided with word parts of speech (PoS) conversion. We use WordNet ontology for determining word level semanticsimilarity while augmenting WordNet with two other lexicographical databases; namely Categorial VariationDatabase (CatVar) and Morphosemantic Database in assisting the word category conversion. Using a humanannotated benchmark data set, all the three approaches achieved a high positive correlation reaching up to (r =0.881647) with comparison to human ratings and two other baselines evaluated on the same benchmark data set. ",

author = "Muhidin Mohamed and Mourad Oussalah",

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AU - Oussalah, Mourad

N1 - This work is licensed under a Creative Commons Attribution 4.0 International License. Page numbers and proceedings footer are added by the organizers. License details: http://creativecommons.org/licenses/by/4.0/

PY - 2014/8/23

Y1 - 2014/8/23

N2 - In this paper, we present a comparison of three methods for taxonomic-based sentence semantic relatedness, aided with word parts of speech (PoS) conversion. We use WordNet ontology for determining word level semanticsimilarity while augmenting WordNet with two other lexicographical databases; namely Categorial VariationDatabase (CatVar) and Morphosemantic Database in assisting the word category conversion. Using a humanannotated benchmark data set, all the three approaches achieved a high positive correlation reaching up to (r =0.881647) with comparison to human ratings and two other baselines evaluated on the same benchmark data set.

AB - In this paper, we present a comparison of three methods for taxonomic-based sentence semantic relatedness, aided with word parts of speech (PoS) conversion. We use WordNet ontology for determining word level semanticsimilarity while augmenting WordNet with two other lexicographical databases; namely Categorial VariationDatabase (CatVar) and Morphosemantic Database in assisting the word category conversion. Using a humanannotated benchmark data set, all the three approaches achieved a high positive correlation reaching up to (r =0.881647) with comparison to human ratings and two other baselines evaluated on the same benchmark data set.

UR - https://www.aclweb.org/anthology/W14-4507

M3 - Unpublished Conference Paper

SP - 37

EP - 42

ER -

A comparative study of conversion aided methods for WordNet sentence textual similarity

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