Homograph Based Synset Sentence

Homograph-based synset sentences are used in natural language processing research to improve the handling of words with multiple meanings (homographs). Current research focuses on leveraging these sentences to enhance model performance in tasks like neural machine translation and natural language inference, often employing transformer-based architectures and pre-training methods incorporating semantic knowledge graphs. This work aims to improve the accuracy and efficiency of NLP systems by better resolving word sense ambiguity, leading to more robust and reliable applications across various domains. The development of improved algorithms and benchmark datasets for synset enrichment and linking across languages is also a key area of ongoing investigation.