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A proposed method for the semantic Similarity use WordNet to handle the ambiguity in Social Media text

Ali Muttaleb, Hasan and Noorhuzaimi@Karimah, Mohd Noor and Rassem, Taha H. and Shahrul Azman, Mohamed Noah and Ahmed Muttaleb, Hasan (2019) A proposed method for the semantic Similarity use WordNet to handle the ambiguity in Social Media text. In: 10th Icatse International Conference on Information Science and Applications 2019, 16 - 18 Disember 2019 , Seoul, Korea. pp. 471-483., 621. ISBN Online 978-981-15-1465-4

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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 1WordNet 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 et al. (2009) ( 2AG203) 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 (Lecture)
Additional Information: Lecture Notes in Electrical Engineering book series
Uncontrolled Keywords: Semantic Similarity; Semantic Relatedness; Taxonomy Relations; Glosses; Semantic Representation; Wordnet 3.1; Social Media; Proposed Method; Ag203.
Subjects: Q Science > QA Mathematics > QA76 Computer software
Faculty/Division: Faculty of Computer System And Software Engineering
Depositing User: Pn. Hazlinda Abd Rahman
Date Deposited: 18 Feb 2020 05:50
Last Modified: 18 Feb 2020 05:50
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