A proposed method using the semantic similarity of WordNet 3.1 to handle the ambiguity to apply in social media text

Ali Muttaleb, Hasan and Noorhuzaimi@Karimah, Mohd Noor and Rassem, Taha H. and Ahmed Muttaleb, Hasan (2020) A proposed method using the semantic similarity of WordNet 3.1 to handle the ambiguity to apply in social media text. In: Information Science and Applications: 10th International Conference on Information Science and Applications (ICISA 2019) , 16-18 December 2019 , Seoul, South Korea. pp. 471-483., 621. ISSN 1876-1100 ISBN 978-981151464-7

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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%.

Item Type: Conference or Workshop Item (Speech)
Additional Information: Part of the Lecture Notes in Electrical Engineering book series
Uncontrolled Keywords: AG203; Glosses; Proposed method; Semantic relatedness; Semantic representation; Semantic similarity; Social media; Taxonomy relations; WordNet 3.1
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Faculty/Division: Faculty of Computing
Institute of Postgraduate Studies
Depositing User: Dr. Taha Hussein Alaaldeen Rassem
Date Deposited: 13 Jul 2020 08:14
Last Modified: 18 Jan 2021 08:19
URI: http://umpir.ump.edu.my/id/eprint/28452
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