Word sense disambiguation for free-text indexing using a massive semantic network
CIKM '93 Proceedings of the second international conference on Information and knowledge management
VisualSEEk: a fully automated content-based image query system
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
WWCA '97 Proceedings of the International Conference on Worldwide Computing and Its Applications
ImageRoadMap: A New Content-based Image Retrieval System
DEXA '97 Proceedings of the 8th International Conference on Database and Expert Systems Applications
Similarity-based methods for word sense disambiguation
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Learning-based linguistic indexing of pictures with 2--d MHMMs
Proceedings of the tenth ACM international conference on Multimedia
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In this paper, we present an efficient term rewriting technique that computes a degree of term to domain relevance. The proposed method resolves the problems in ontology integrated concept search. Those problems are (i) Pre-defined concept classes in ontology are not relevant to users (no proper concept class for a target annotation has not found). (ii) Too many similar concept classes are provided to a user therefore, a user may fail to choose a correct semantic class for a target annotation (ordinary users are not an expert in concept classification). The method uses sense disambiguation task for finding relevant terms for a given domain. Sense disambiguation requires term-to-term similarity measurement and term frequency measurement. For fair modeling of not observed term frequencies, discounting and redistribution model is applied. The proposed method is a compliment to our previous work presented in [13][14]. Robustness of our method is demonstrated through human judgment test that shows our method allows prediction of precise term list (overall 75% of correct prediction) that are relevant to a given domain.