Second-Order HMM for Event Extraction from Short Message

  • Huixing Jiang
  • Xiaojie Wang
  • Jilei Tian
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6177)


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.


Hide Markov Model Machine Translation Chinese Character Short Message Service Short Message 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Huixing Jiang
    • 1
  • Xiaojie Wang
    • 1
  • Jilei Tian
    • 2
  1. 1.Center of Intelligence Science and TechnologyBeijing University of Posts and TelecommunicationsBeijingChina
  2. 2.Nokia Research CenterBeijingChina

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