A Hybrid Approach to Word Sense Disambiguation Combining Supervised and Unsupervised Learning
This work addresses the data scarcity issue in NLP for researchers, but it is incremental as it builds on existing methods.
The paper tackles the problem of Word Sense Disambiguation by addressing the limitation of insufficient hand-tagged data in supervised approaches, proposing a hybrid method that combines Modified Lesk and Bag-of-Words with dynamic data enrichment, and reports that it outperforms individual methods in experiments.
In this paper, we are going to find meaning of words based on distinct situations. Word Sense Disambiguation is used to find meaning of words based on live contexts using supervised and unsupervised approaches. Unsupervised approaches use online dictionary for learning, and supervised approaches use manual learning sets. Hand tagged data are populated which might not be effective and sufficient for learning procedure. This limitation of information is main flaw of the supervised approach. Our proposed approach focuses to overcome the limitation using learning set which is enriched in dynamic way maintaining new data. Trivial filtering method is utilized to achieve appropriate training data. We introduce a mixed methodology having Modified Lesk approach and Bag-of-Words having enriched bags using learning methods. Our approach establishes the superiority over individual Modified Lesk and Bag-of-Words approaches based on experimentation.