Online named entity recognition method for microtexts in social networking services: A case study of twitter

Expert Systems with Applications

Volumn 39, Issue 9, 2012, Pages 8066-8070

  Jung, Jason J ^a  

^a Yeungnam University   (South Korea)

Author keywords

Multiplex social network; Named entity recognition; Network analysis; Social networks

Indexed keywords

EFFICIENT STRATEGY; NAMED ENTITY RECOGNITION; NER SYSTEM; ONLINE SOCIAL NETWORKS; SOCIAL MEDIA; SOCIAL NETWORKS;

ELECTRIC NETWORK ANALYSIS; INFORMATION SYSTEMS; MATHEMATICAL MODELS;

SOCIAL NETWORKING (ONLINE);

EID: 84858342816     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2012.01.136     Document Type: Article

References (15)

1. Y. Benajiba, P. Rosso, and J.-M. Benedí Anersys: An arabic named entity recognition system based on maximum entropy A.F. Gelbukh, Proceedings of the 8th International Conference on Computational Linguistics and Intelligent Text Processing (CICLing 2007), Mexico City, Mexico, February 18-24 Lecture Notes in Computer Science vol. 4394 2007 Springer 143 153
2. A.L. Berger, S.D. Pietra, and V.J. Della Pietra A maximum entropy approach to natural language processing Computational Linguistics 22 1 1996 39 71
3. H.L. Chieu, and H.T. Ng Named entity recognition with a maximum entropy approach Proceedings of the 7th international conference on natural language learning (CONLL'03), Edmonton, Canada 2003 ACL 160 163
4. J.J. Jung Integrating social networks for context fusion in mobile service platforms Journal of Universal Computer Science 16 15 2010 2099 2110
5. J.J. Jung Reusing ontology mappings for query segmentation and routing in semantic peer-to-peer environment Information Sciences 180 17 2010 3248 3257
6. J.J. Jung Service chain-based business alliance formation in service-oriented architecture Expert Systems with Applications 38 3 2011 2206 2211
7. J.J. Jung Evolutionary approach for semantic-based query sampling in large-scale information sources Information Sciences 182 1 2012 30 39
8. Jung, J. J. (2012b). Maximum entropy-based named entity recognition method for multiple social networking services. Journal of Universal Computer Science, 18(2), in press.
9. L. Li, R. Zhou, and D. Huang Two-phase biomedical named entity recognition using crfs Computational Biology and Chemistry 33 4 2009 334 338
10. C. Meyer, and H. Schramm Boosting hmm acoustic models in large vocabulary speech recognition Speech Communication 48 5 2006 532 548
11. Jesus Oliva, Jose Ignacio Serrano, María Dolores del Castillo, and Ángel Iglesias Symss: A syntax-based measure for short-text semantic similarity Data & Knowledge Engineering 70 4 2011 390 405
12. S.K. Saha, S. Narayan, S. Sarkar, and P. Mitra A composite kernel for named entity recognition Pattern Recognition Letters 31 12 2010 1591 1597
13. M.J. Silva, B. Martins, M. Chaves, A.P. Afonso, and N. Cardoso Adding geographic scopes to web resources Computers, Environment and Urban Systems 30 4 2006 378 399
14. B. Sriram, D. Fuhry, E. Demir, H. Ferhatosmanoglu, and M. Demirbas Short text classification in twitter to improve information filtering F. Crestani, S. Marchand-Maillet, H.-H. Chen, E.N. Efthimiadis, J. Savoy, Proceeding of the 33rd international ACM SIGIR conference on research and development in information retrieval (SIGIR 2010), Geneva, Switzerland, July 19-23 2010 ACM 841 842
15. Zhu, J.; Goncalves, A.; Uren, V.; Motta, E.; & Pacheco, R. (2005). Corder: Community relation discovery by named entity recognition. In Proceedings of the 2005 international conference on knowledge capture (K-CAP) (pp. 219-220).