Andrew Neel

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Knowledge of chemical reaction mechanisms can facilitate catalyst optimization, but extracting that knowledge from a complex system is often challenging. Here, we present a data-intensive method for deriving and then predictively applying a mechanistic model of an enantioselective organic reaction. As a validating case study, we selected an intramolecular(More)
The problem of recognizing textual entailment (RTE) has been recently addressed with some success using semantic models. That attempt to capture the complexity of world knowledge. (Neel et al., 2008) has shown that semantic graphs made of synsets and selected relationships between them enable fairly simple methods to provide very competitive performance for(More)
The problem of recognizing textual entailment (RTE) has been recently addressed using syntactic and lexical models with some success. Here, we further explore this problem, this time using the world knowledge captured in large semantic graphs such as WordNet. We show that semantic graphs made of synsets and selected relationships between them enable fairly(More)