The Impact of Part-of-Speech Tagging on the Accuracy of Word Sense Disambiguation Models
JSHS · 2022
Overview
Billions of people use search engines, like Google, daily. These systems identify meanings of words using machine learning classifiers (MLCs) through a task called word sense disambiguation (WSD). MLCs are algorithms that identify patterns in data to infer the definitions of words. A task relevant to WSD is part-of- speech tagging, which identifies the part-of-speech of a word using algorithms called part-of-speech taggers (POSTs). Past research on WSD and part-of-speech tagging indicates that using POSTs while training MLCs increases the MLCs’ accuracy. However, the specific changes in accuracy that different POSTs cause in MLCs is not up-to-date with the MLCs and POSTs that are currently widely used. I aimed to study the relationship between popular MLCs and POSTs to provide information that is current. I explored the effect of two different POSTs, the Natural Language Toolkit Tagger and the Stanford Tagger, on the accuracy of three different MLCs: the naive Bayes, random forest, and k-NN classifiers. I used the SemCor 3.0 dataset, and all of my objectives were met. My findings supported conclusions from Moreno-Monteagudo et al. (2006), which stated that POSTs increase the accuracy of MLCs by a marginal amount, and certain combinations of POSTs and MLCs, such as the Stanford Tagger and the k-NN classifier, are more accurate than others. My study also found that the Stanford Tagger increased the accuracy of MLCs the most, and the k-NN classifiers outperformed all other MLCs tested. There were also instances where the accuracy decreased, which may be due to overfitting.
Competition history
- JSHS 2022
Resources
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