As natural language understanding research advances towards deeper knowledge modeling, the tasks become more and more complex: we are interested in more nuanced word characteristi...
Radu Florian, Hongyan Jing, Nanda Kambhatla, Imed ...
We present a simple, two-steps supervised strategy for the identification and classification of thematic roles in natural language texts. We employ no external source of informat...
Information workers often have to balance many tasks and interruptions. In this work, we explore peripheral display techniques that improve multitasking efficiency by helping user...
Tara Matthews, Mary Czerwinski, George G. Robertso...
We report on results of combining graphical modeling techniques with Information Extraction resources (Pattern Dictionary and Lexicon) for both frame and semantic role assignment....
The goal of our research is to improve event extraction by learning to identify secondary role filler contexts in the absence of event keywords. We propose a multilayered event e...