Homograph Disambiguation

Homograph disambiguation, the task of resolving the meaning of words with identical spellings but different meanings, is a crucial challenge in natural language processing. Current research focuses on leveraging contextualized word embeddings from pre-trained language models (like BERT and Transformer architectures) and incorporating these into various machine learning models, including deep learning approaches, to improve disambiguation accuracy. This work is driven by the need to enhance the performance of downstream tasks such as machine translation and information retrieval, where accurate word sense identification is critical. The development of new datasets and evaluation benchmarks for various languages is also a significant area of ongoing research, facilitating more robust and comparable model evaluations.