Artificial Intelligence

    

Using Table based Version of K Nearest Neighbor for Classifying Words Semantically

Authors: Taeho Jo

This article proposes the modified KNN (K earest Neighbor) algorithm which receives a table as its input data and is applied to the word categorization. The motivations of this research are the successful results from applying the table based algorithms to the text categorizations in previous works and the expectation of synergy effect between the text categorization and the word categorization. In this research, we define the similarity metricbetween two tables representing words, modify the KNN algorithm by replacing the exiting similarity metric by the proposed one, andapply it to the word categorization. The proposed KNN is empirically validated as the better approach in categorizing words in newsarticles and opinions. In using the table based KNN algorithm, it is easier to trace results from categorizing words.

Comments: 11 Pages.

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Submission history

[v1] 2024-05-26 05:12:24

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