Token identification using HMM and PPM models

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2903, Issue , 2003, Pages 173-185

  Wen, Yingying ^a,b   Witten, Ian H ^b   Wang, Dianhui ^c  

^a MONASH UNIVERSITY   (Australia)

^b UNIVERSITY OF WAIKATO   (New Zealand)

^c LA TROBE UNIVERSITY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; LEARNING SYSTEMS; LINGUISTICS; MARKOV PROCESSES; MATHEMATICAL MODELS;

EXISTING SYSTEMS; F MEASURE; HIDDEN MARKOV MODELS (HMMS); IDENTIFICATION MODEL; LANGUAGE INDEPENDENTS; LANGUAGE PROCESSING; PREDICTION BY PARTIAL MATCHING; RETARGETABILITY;

HIDDEN MARKOV MODELS; ARTIFICIAL INTELLIGENCE;

HIDDEN MARKOV MODELS (HMM); LANGUAGE PROCESSING TASKS; PARTIAL MATCHING MODELS (PPM); TOKEN IDENTIFICATION;

EID: 0346325952     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-24581-0_15     Document Type: Conference Paper

References (15)

1. Sekine, S.: NYU: Description of the Japanese NE system used for MET-2. In: Proceedings of MUC-7 1998. (1998)
2. Bennett, S.W., Aone, C., Lovell, C.: Learning to tag multilingual texts through observation. In: Proceedings of the Second Conference on Empirical Methods in Natural Language Processing, Providence, Rhode Island (1997) 109–116
3. Baluja, S., Mittal, V.O., Sukthankar, R.: Applying machine learning for high performance named-entity extraction. In: Proceedings of the Conference of the Pacific Association for Computational Linguistics, Waterloo, CA (1999) 365–378
4. Borthwick, A., Sterling, J., Agichtein, E., Grishman, R.: Exploiting diverse knowledge sources via maximum entropy in named entity recognition. In: Proceedings of the Sixth Workshop on Very Large Corpora, Montreal, Canada (1998)
5. Viterbi, A.J.: Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Transactions on Information Theroy IT-13 (1967) 260–269
6. Mikheev, A., Moens, M., Grover, C.: Named entity recognition without gazetteers. In: Proceedings of EACL, Bergen, Norway. (1999)
7. Bikel, D.M., Schwartz, R., Weischedel, R.M.: An algorithm that learns what’s in a name. Machine Learning Journal 34 (1999) 211–231
8. Rabiner, L.R.: A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE 77 (1989) 257–286
9. Witten, I.H., Moffat, A., Bell, T.C.: Managing Gigabytes-Compressing and Indexing Documents and Images. 2 edn. ISBN 1-55860-570-3. Morgan Kaufmann Publishers, San Francisco, California (1999)
10. Cleary, J.G., Witten, I.H.: Data compression using adaptive coding and partial string matching. IEEE Trans on Communications 32 (1984) 396–402
11. Forney, J.G.D.: The Viterbi algorithm. Proceedings of the IEEE 61 (1973) 268–278
12. Witten, I.H., Bell, T.C.: The zero-frequency problem: Estimating the probabilities of novel events in adaptive text compression. IEEE Transactions on Information Theory 37 (1991) 1085–1093
13. Teahan, W.J., Wen, Y., McNab, R., Witten, I.H.: A compression-based algorithm for Chinese word segmentation. Computational Linguistics 26 (2000) 375–393 177
14. Van Rijsbergen, C.J.: Information Retrieval. Second edn. Butterworths, London (1979)
15. Lewis, D.D.: Evaluating and optimizing autonomous text classification systems. In: Proceedings of the Eighteenth Annual International ACM Special Interest Group on Information Retrieval. (1995) 246–254