NIL is not nothing: Recognition of Chinese network informal language expressions

SIGHAN@IJCNLP 2005 - 4th SIGHAN Workshop on Chinese Language Processing, Proceedings of the Workshop

Volumn , Issue , 2005, Pages 95-102

  Xia, Yunqing ^a   Wong, Kam Fai ^a   Gao, Wei ^a  

^a CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

[No Author keywords available]

Indexed keywords

NATURAL LANGUAGE PROCESSING SYSTEMS; RESOURCE VALUATION; SUPPORT VECTOR MACHINES; TUNGSTEN COMPOUNDS;

CHAT ROOMS; EXPRESSION RECOGNITION; IN NETWORKS; MATCHING METHODS; MEDIATED COMMUNICATION; NETWORK INFORMAL LANGUAGES; PATTERN-MATCHING; PROCESSING APPROACH; PROCESSING NETWORK; UNCONVENTIONAL RESOURCES;

PATTERN MATCHING;

EID: 49049117378     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (16)

1. Bender, O., Och, F. J. and Ney, H. 2003. Maximum Entropy Models for Named Entity Recognition, CoNLL-2003, pp. 148-151.
2. Chieu, H. L. and Ng, H. T. 2002. Named Entity Recognition: A Maximum Entropy Approach Using Global Information. COLING-02, pp. 190-196.
3. Danet, B. 2002. The Language of Email, European Union Summer School, University of Rome.
4. Isozaki, H. and Kazawa, H. 2002. Efficient Support Vector Classifiers for Named Entity Recognition, COLING-02, pp. 390-396..
5. Joachims, T. 1998. Text categorization with Support Vector Machines: Learning with many relevant features. ECML’98, pp. 137-142.
6. Kudo, T. and Matsumoto, Y. 2001. Chunking with Support Vector Machines. NAACL 2001, pp.192-199.
7. Mayfield, J. 2003. Paul McNamee; Christine Piatko, Named Entity Recognition using Hundreds of Thousands of Features, CoNLL-2003, pp. 184-187.
8. McElhearn, K. 2000. Writing Conversation - An Analysis of Speech Events in E-mail Mailing Lists, http://www.mcelhearn.com/cmc.html, Revue Française de Linguistique Appliquée, volume V-1.
9. Nishimura, Y. 2003. Linguistic Innovations and Interactional Features of Casual Online Communication in Japanese, JCMC 9 (1).
10. Pailouras, G., Karkaletsis, V. and Spyropoulos, C. D. 2000. Learning Decision Trees for Named-Entity Recognition and Classification. Workshop on Machine Learning for Information Extraction, ECAI(2000).
11. Sekine, S., Grishman, R. and Shinnou, H. 1998. A Decision Tree Method for Finding and Classifying Names in Japanese Texts, WVLC 98.
12. Snitt, E. N. 2000. The Use of Language on the Internet, http://www.eng.umu.se/vw2000/Emma/linguistics1.htm.
13. Sproat, R., Black, A., Chen, S., Kumar, S., Ostendorf, M. and Richards, M. 2001. Normalization of Nonstandard Words. Computer Speech and Languages, 15(3):287- 333.
14. Takeuchi, K. and Collier, N. 2002. Use of Support Vector Machines in Extended Named Entity Recognition. CoNLL-2002, pp. 119-125.
15. Zhang, Z., Yu, H., Xiong, D. and Liu, Q. 2003. HMM-based Chinese Lexical Analyzer ICTCLAS. In the 2nd SIGHAN workshop affiliated with ACL’03, pp. 184-187.
16. Zhao, S. 2004. Named Entity Recognition in Biomedical Texts Using an HMM model, COLING-04 workshop on Natural Language Processing in Biomedicine and its Applications.