Automatic Enrichment of Semantic Relation Network and Its Application to Word Sense Disambiguation
Images are the most common contents on the Internet for a long time. Lots of researchers have been studied to satisfy user demands for semantic visual recognition using low-level feature (such as color or texture) or keywords which were textual annotations but still challenging. Keywords in images give great evidence to identify what images are. Keywords are not always related with image its own. It is necessary to remove those irrelevant keywords and give higher values to relevant keywords using statistical models and knowledge base such as WordNet. For this reason, we propose a modified WUP similarity measurement in WordNet to decide which keywords are close to image. To identify irrelevant keywords, we use various semantic similarity measures between keywords and image titles. We focus on solving word sense disambiguation of image titles (such as bat, mouse, jaguar, etc). The results show that by augmenting knowledge-based with proposed method we can remove irrelevant images and take a further step to solve the WSD problem.