Semantic role labelling of English tweets through sentence boundary detection

Social media service like Twitter has become a trendy communication medium for online users to share quick and up-to-date information. However, the tweets are extremely noisy, full of spelling and grammatical mistakes which pose unique challenges towards semantic information extraction. One prospective solution to this problem is semantic role labelling (SRL), which focuses on unifying variations in the facade syntactic forms of semantic relations. SRL for tweets plays central role in a wide range of tweet related applications associated with semantic information extraction. In this paper, we proposed an automatic SRL system for English tweets by identifying sentences and using sequential minimal optimisation (SMO). We conducted experiments on our SRL annotated dataset to evaluate proposed approach and report better performance than existing state-of-the-art SRL systems for English tweets.