Semantic Role Labeling of English Tweets

Abstract

Semantic role labeling (SRL) is a task of defining the conceptual role to the arguments of predicate in a sentence. This is an important task for a wide range of tweet related applications associated with semantic information extraction. SRL is a challenging task due to the difficulties regarding general semantic roles for all predicates. It is more challenging for Social Media Text (SMT) where the nature of text is more casual. This paper presents an automatic SRL system for English tweets based on Sequential Minimal Optimization (SMO) algorithm. Proposed system is evaluated through experiments and reports comparable performance with the prior state-of-the art SRL system.