Towards a Robust Deep Language Understanding System

Abstract

We propose a system that bridges the gap between the two major approaches toward natural language processing: robust shallow text processing and domain-specific (often linguistically-based) deep understanding. We propose to use an existing linguistically motivated deep understanding system as the core and to leverage statistical techniques and external resources such as world knowledge to broaden coverage and increase robustness. We will also develop a semantic representation framework, which supports underspecification, granularity and incrementality, the critical factors of robustness in representing natural language semantics.