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Evaluation of parts-of-speech classification and effectiveness using ambiguity metrics in a hybrid model

artificial-intelligence
data-mining-and-machine-learning
text-mining

Abstract

Enhancing translation accuracy through a hybrid methodology has proven to be more effective. The term "ambiguous words" refers to words with multiple distinct meanings. This study focuses on word sense disambiguation (WSD), specifically addressing parts-of-speech ambiguity that arises when an ambiguous word can function as a noun, verb, adjective, adverb, or preposition.

The proposed technique for word meaning disambiguation is designed for source-to-target machine translation and is built using supervised WSD text classification algorithms. These include decision trees, support vector machines, and the naïve Bayes classifier. The model was evaluated using the ten-fold cross-validation test technique, yielding an efficiency rate of approximately 87%.

Additionally, the AmbiF model demonstrated superior accuracy, recall, and F-score compared to other supervised machine learning classification methods. All experiments were conducted using the WEKA machine learning tool to analyze the algorithms and assess the performance of the AmbiF model.

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