Bootstrapping semantics from text is one of the greatest challenges in natural language learning. We first define a word similarity measure based on the distributional pattern of ...
This paper presents a cluster-based text categorization system which uses class distributional clustering of words. We propose a new clustering model which considers the global in...
This paper describes a system for efficient indexing and retrieval of words in collections of document images. The proposed method is based on two main principles: unsupervised pr...
This paper presents a new clustering algorithm called DSCBC which is designed to automatically discover word senses for polysemous words. DSCBC is an extension of CBC Clustering [...
Noriko Tomuro, Steven L. Lytinen, Kyoko Kanzaki, H...
Vector-based models of lexical semantics retrieve semantically related words automatically from large corpora by exploiting the property that words with a similar meaning tend to ...