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A Proposed Method Using the Semantic Similarity of WordNet 3.1 to Handle the Ambiguity to Apply in Social Media Text

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Information Science and Applications

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 621))

Abstract

The semantic similarity between two concepts is widely used in natural language processing. In this article, we propose a method using WordNet 3.1 to determine the similarity based on feature combinations. This work focuses on overcoming the ambiguity in social media text via the selection of informative features to improve semantic representation. In addition, this research uses social media as its research domain used in this work, and the study is only limited to the politic dataset. A feature-based method is applied to predict the outcome and improve the performance of the proposed method depending on factors related to the fidelity, continuity, and balance of knowledge sources in WordNet 3.1. Semantic similarity measurements among words are insufficient and unbalanced features. However, this study presents a semantic similarity measure of a feature-based method in WordNet 3.1 to determine the similarity between two concepts/words depending on the selected features used to measure their similarity, which is also known as a “noun” and “is-a” relations-based method. We evaluate our proposed method using the data set in Agirre [1] (AG203) and compare our results of our new method as which three of methods taxonomy relation, non-taxonomy and Glosses with those of related studies. The correlation with human judgments is subjective and low based on our results was a better. Experimental results show that our new method significantly outperforms other existing computational methods with the following results: r = 0.73%, p = 0.69%, m = 0.71% and nonzero = 0.95%.

https://wordnet.princeton.edu.

https://github.com/alimuttaleb/Ali-Muttaleb/compare/Semantic-Similarity-by-using-theambiguity-AG203.

This work is supported by the University of Malaysia Pahang (UMP) via research grant UMP (RDU1803141, PGRS190398).

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Notes

  1. 1.

    https://wordnet.princeton.edu/.

  2. 2.

    https://github.com/alimuttaleb/Ali-Muttaleb/blob/master/Synonym.txt.

  3. 3.

    https://github.com/alimuttaleb/Ali-Muttaleb/blob/master/Hypernym.

  4. 4.

    https://github.com/alimuttaleb/Ali-Muttaleb/blob/master/Glosses.

  5. 5.

    https://github.com/alimuttaleb/Ali-Muttaleb/blob/master/Non-taxonomy.

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Acknowledgements

This work is supported by the University Malaysia Pahang (UMP) via Research Grant UMP (RDU1803141, PGRS190398).

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Authors and Affiliations

  1. Faculty of Computing, University Malaysia Pahang, 26300, Gambang, Kuantan, Pahang Darul Makmur, Malaysia

    Ali Muttaleb Hasan, Noorhuzaimi Mohd Noor, Taha Hussein Rassem & Ahmed Muttaleb Hasan

  2. Faculty of Information Science and Technology, University Kebangsaan Malaysia (UKM), 43600, Bangi Selangor, Malaysia

    Shahrul Azman Mohd Noah

Authors
  1. Ali Muttaleb Hasan
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  2. Noorhuzaimi Mohd Noor
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  3. Taha Hussein Rassem
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  4. Shahrul Azman Mohd Noah
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  5. Ahmed Muttaleb Hasan
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Correspondence to Noorhuzaimi Mohd Noor .

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Editors and Affiliations

  1. Kyonggi University, Suwon-si, Korea (Republic of)

    Kuinam J. Kim

  2. School of Games, Hongik University, Chungchengnam-do, Korea (Republic of)

    Hye-Young Kim

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Hasan, A.M., Mohd Noor, N., Rassem, T.H., Mohd Noah, S.A., Hasan, A.M. (2020). A Proposed Method Using the Semantic Similarity of WordNet 3.1 to Handle the Ambiguity to Apply in Social Media Text. In: Kim, K., Kim, HY. (eds) Information Science and Applications. Lecture Notes in Electrical Engineering, vol 621. Springer, Singapore. https://doi.org/10.1007/978-981-15-1465-4_47

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  • DOI: https://doi.org/10.1007/978-981-15-1465-4_47

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  • Online ISBN: 978-981-15-1465-4

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