In this work, we propose a variant of a well-known instance-based algorithm: WKNN. Our idea is to exploit task-dependent features in order to calculate the weight of the instances according to a novel paradigm: the Textual Attraction Force, that serves to quantify the degree of relatedness between documents. The proposed method was applied to a challenging text classification task: irony detection. We experimented with corpora in the state of the art. The obtained results show that despite being a simple approach, our method is competitive with respect to more advanced techniques.

A knowledge-based weighted KNN for detecting Irony in Twitter

Patti, Viviana
2018-01-01

Abstract

In this work, we propose a variant of a well-known instance-based algorithm: WKNN. Our idea is to exploit task-dependent features in order to calculate the weight of the instances according to a novel paradigm: the Textual Attraction Force, that serves to quantify the degree of relatedness between documents. The proposed method was applied to a challenging text classification task: irony detection. We experimented with corpora in the state of the art. The obtained results show that despite being a simple approach, our method is competitive with respect to more advanced techniques.
2018
17th Mexican International Conference on Artificial Intelligence, MICAI 2018
Guadalajara, Mexico
2018
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Springer Verlag
11289
194
206
9783030044961
https://link.springer.com/chapter/10.1007%2F978-3-030-04497-8_16
Instance-based algorithm; Irony detection; WKNN; Theoretical Computer Science; Computer Science (all)
Hernández Farías, Delia Irazú*; Montes-y-Gómez, Manuel; Escalante, Hugo Jair; Rosso, Paolo; Patti, Viviana
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1698250
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