Authors: Dwijen Rudrapal; Amitava Das
Addresses: Department of Computer Science and Engineering, NIT Agartala, India ' Department of Computer Science and Engineering, IIIT Sricity, India
Abstract: 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.
Keywords: tweet stream; semantic role labelling; SRL; tweet summarisation; machine learning algorithm.
International Journal of Intelligent Information and Database Systems, 2018 Vol.11 No.4, pp.225 - 235
Received: 30 Aug 2017
Accepted: 12 Apr 2018
Published online: 04 Dec 2018 *