Second-Order HMM for Event Extraction from Short Message

Abstract

This paper presents a novel integrated second-order Hidden Markov Model (HMM) to extract event related named entities (NEs) and activities from short messages simultaneously. It uses second-order Markov chain to better model the context dependency in the string sequence. For decoding second-order HMM, a two-order Viterbi algorithm is used. The experiments demonstrate that combing NE and activities as an integrated model achieves better results than process them separately by NER for NEs and POS decoding for activities. The experimental results also showed that second-order HMM outperforms than first-order HMM. Furthermore, the proposed algorithm significantly reduces the complexity that can run in the handheld device in the real time.