Large-Scale Semantic Text Understanding

Semantic Text Understanding involves linguistic based processing of text and transforming it into a conceptual representation of its meaning. In this paper, we introduce a potential system for Text Understanding that is highly scalable that takes as input unstructured text and produces conceptual graph representation of its meaning. Several experiments were conducted to gain explanatory insights into its scalability and performance. Varied test scenarios and configuration setups were experimented. Through these experiments we will demonstrate how the configuration of the system deployed affect the scalability and performance.