A REVIEW OF WORD SENSE DISAMBIGUATION METHOD

Abstract

Background: Word Sense Disambiguation (WSD) is known to have a detrimental effect on the precision of information retrieval systems, where WSD is the ability to identify the meanings of words in context. There is a challenge in inference-correct-sensing on ambiguous words. Through many years of research, there have been various solutions to WSD that have been proposed; they have been divided into supervised and knowledge-based unsupervised. Objective: The first objective of this study was to explore the state-of-art of the WSD method with a hybrid method using ontology concepts. Then, with the findings, we may understand which tools are available to build WSD components. The second objective was to determine which method would be the best in giving good performance results of WSD, by analysing how the methods were used to answer specific WSD questions, their production, and how their performance was analysed. Methods: A review of the literature was conducted relating to the performance of WSD research, which used a comparison method of information retrieval analysis. The study compared the types of methods used in case, and examined methods for tools production, tools training, and analysis of performance. Results: In total 12 papers were found that satisfied all 3 inclusion criteria, and there was an anchor paper assigned to be referred. We chose the knowledge-based unsupervised approach because it has fewer word sets constraints than the supervised approaches which require training data. Concept-based ontology will help WSD in finding the semantic words concept with respect to another concept around it. Conclusion: Many methods was explored and compared to determine the most suitable way to build a WSD model based on semantics between words in query texts that can be related to the knowledge concept by using ontological knowledge presentation.