JobimText visualizer: A Graph-based Approach to Contextualizing Distributional Similarity

Abstract

We introduce an interactive visualization component for the JoBimText project. JoBimText is an open source platform for large-scale distributional semantics based on graph representations. First we describe the underlying technology for computing a distributional thesaurus on words using bipartite graphs of words and context features, and contextualizing the list of semantically similar words towards a given sentential context using graph-based ranking. Then we demonstrate the capabilities of this contextualized text expansion technology in an interactive visualization. The visualization can be used as a semantic parser providing contextualized expansions of words in text as well as disambiguation to word senses induced by graph clustering, and is provided as an open source tool.