The Impact of Part-of-Speech Tagging on the Accuracy of Word Sense Disambiguation Models
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
Related projects
CSEF · 2015
Improving the Accuracy of Sentiment Classification: A Novel Synthesis of Computational and Analytical Methods
CSEF · 2014
A Machine Learning Model for Automated Semantic Short Essay Assessment through Random Forest Based Ensembles and NLP
CSEF · 2017
Predicting Formality of Written Texts Using Machine Learning Algorithms
CSEF · 2011
The Application of Bayesian Networks for Speech Classification
ISEF · 2015
#feels: Detecting and Visualizing Regional Sentiment from Cross-lingual Tweets for Specific Hashtags Using SVM and Naive Bayes Classifiers
CSEF · 2016
A Sense of Stylometry
CSEF · 2013
The Development and Study of an Algorithm to Explain Successful Language Acquisition from an Inconsistent Source
ISEF · 2025
Bridging Linguistic Divides: Cross-Lingual Transfer Learning and Pseudo-Labeling for Natural Language Processing in Low-Resource Languages
Closest projects by meaning, across every fair and year in the corpus.