Decoding algorithm in statistical machine translation
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
A second-order Hidden Markov Model for part-of-speech tagging
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
An efficient A* search algorithm for statistical machine translation
DMMT '01 Proceedings of the workshop on Data-driven methods in machine translation - Volume 14
A New Fuzzy Support Vector Machine Method for Named Entity Recognition
ICCSIT '08 Proceedings of the 2008 International Conference on Computer Science and Information Technology
Chinese Named Entity Recognition with CRFs: Two Levels
CIS '08 Proceedings of the 2008 International Conference on Computational Intelligence and Security - Volume 02
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Word graphs for statistical machine translation
ParaText '05 Proceedings of the ACL Workshop on Building and Using Parallel Texts
Development of a POS Tagger for Malayalam - An Experience
ARTCOM '09 Proceedings of the 2009 International Conference on Advances in Recent Technologies in Communication and Computing
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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.