Word sense disambiguation via high order of learning in complex networks

TL;DR

This paper introduces a hybrid complex network approach combining low- and high-level learning to improve supervised word sense disambiguation accuracy, demonstrating the importance of semantic pattern recognition.

Contribution

It presents a novel hybrid learning framework within complex networks for word disambiguation, integrating high-order learning to enhance classification performance.

Findings

High-level learning improves disambiguation accuracy.

Internal word structures reveal patterns beyond traditional methods.

Decision boundary analysis shows the impact of classifier weightings.

Abstract

Complex networks have been employed to model many real systems and as a modeling tool in a myriad of applications. In this paper, we use the framework of complex networks to the problem of supervised classification in the word disambiguation task, which consists in deriving a function from the supervised (or labeled) training data of ambiguous words. Traditional supervised data classification takes into account only topological or physical features of the input data. On the other hand, the human (animal) brain performs both low- and high-level orders of learning and it has facility to identify patterns according to the semantic meaning of the input data. In this paper, we apply a hybrid technique which encompasses both types of learning in the field of word sense disambiguation and show that the high-level order of learning can really improve the accuracy rate of the model. This evidence serves to demonstrate that the internal structures formed by the words do present patterns that, generally, cannot be correctly unveiled by only traditional techniques. Finally, we exhibit the behavior of the model for different weights of the low- and high-level classifiers by plotting decision boundaries. This study helps one to better understand the effectiveness of the model.