A unified framework to identify and extract uncertainty cues, holders, and scopes in one fell-swoop

Abstract

We present a unified framework based on supervised sequence labelling methods to identify and extract uncertainty cues, holders, and scopes in one-fell swoop with an application on Arabic tweets. The underlying technology employs Support Vector Machines with a rich set of morphological, syntactic, lexical, semantic, pragmatic, dialectal, and genre-specific features, and yields an average F 1 score of 0.759.