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

The Extended Arabic WordNet: a Case Study and an Evaluation using a Word Sense Disambiguation System

Arabic WordNet (AWN) represents one of the best-known lexical resources for the Arabic language. However, it contains various issues that affect its use in different Natural Language Processing (NLP) applications. Due to…

LU-BZU at SemEval-2021 Task 2: Word2Vec and Lemma2Vec performance in Arabic Word-in-Context disambiguation

This paper presents a set of experiments to evaluate and compare between the performance of using CBOW Word2Vec and Lemma2Vec models for Arabic Word-in-Context (WiC) disambiguation without using sense inventories or sens…

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 Using Word Embeddings in General and Specific Domains

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…