Semi-Supervised Word Sense Disambiguation Using Word Embeddings in General and Specific Domains

Semi-supervised Learning for Word Sense Disambiguation

This work is a study of the impact of multiple aspects in a classic unsupervised word sense disambiguation algorithm. We identify relevant factors in a decision rule algorithm, including the initial labeling of examples,…

Semi-supervised Word Sense Disambiguation with Neural Models

Determining the intended sense of words in text - word sense disambiguation (WSD) - is a long standing problem in natural language processing. Recently, researchers have shown promising results using word vectors extract…

PoKED: A Semi-Supervised System for Word Sense Disambiguation

Word Sense Disambiguation (WSD) is an open problem in Natural Language Processing, which is challenging and useful in both supervised and unsupervised settings where all the words in any given text need to be disambiguat…

A Semi-Supervised Method for Arabic Word Sense Disambiguation Using a Weighted Directed Graph

Semi-supervised Word Sense Disambiguation Using Example Similarity Graph

Word Sense Disambiguation (WSD) is a well-known problem in the natural language processing. In recent years, there has been increasing interest in applying neural net-works and machine learning techniques to solve WSD pr…