Efficient inference on sequence segmentation models

ACM International Conference Proceeding Series

Volumn 148, Issue , 2006, Pages 793-800

  Sarawagi, Sunita ^a  

^a INDIAN INSTITUTE OF TECHNOLOGY BOMBAY   (India)

Author keywords

[No Author keywords available]

Indexed keywords

OVERLAPPING SEGMENTS; SEGMENTATION MODELS; SEQUENCE SEGMENTATION;

COMPUTATION THEORY; DYNAMIC PROGRAMMING; FEATURE EXTRACTION; MATHEMATICAL MODELS;

COMPUTER SIMULATION;

EID: 34250739895     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1143844.1143944     Document Type: Conference Paper

References (10)

1. Bartlett, P. L., Collins, M., Taskar, B., & McAllester, D. (2005). Exponentiated gradient algorithms for large-margin structured classification. In L. K. Saul, Y. Weiss and L. Bottou (Eds.), Advances in neural information processing systems 17, 113-120. Cambridge, MA: MIT Press.
2. Borthwick, A., Sterling, J., Agichtein, E., & Grishman, R. (1998). Exploiting diverse knowledge sources via maximum entropy in named entity recognition. Sixth Workshop on Very Large Corpora New Brunswick, New Jersey. Association for Computational Linguistics.
3. Cohen, W. W., Ravikumar, P., &: Fienberg, S. E. (2003). A comparison of string distance metrics for name-matching tasks. Proceedings of the IJCAI-2003 Workshop on Information Integration on the Web (IIWeb-09). To appear.
4. DauméIII, H., &: Marcu, D. (2005). Learning as search optimization: approximate large margin methods for structured prediction. ICML '05: Proceedings of the 22nd international conference on Machine learning (pp. 169-176).
5. Keshet, J., Shalev-Shwartz, S., & Singer, Y. (2005). Phoneme alignment using large margin techniques. Workshop on the Advances in Structured Learning for Text and Speech Processing. NIPS.
6. McDonald, R., Crammer, K., & Pereira, F. (2005). Flexible text segmentation with structured multilabel classification. Human Language Technology Conference Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP).
7. Peng, F., & McCallum, A. (2004). Accurate information extraction from research papers using conditional random fields. HLT-NAACL (pp. 329-336).
8. Sarawagi, S., &: Cohen, W. W. (2004). Semi-markov conditional random fields for information extraction. NIPS.
9. Tsochantaridis, I., Joachims, T., Hofmann, T., & Altun, Y. (2005). Large margin methods for structured and interdependent output variables. Journal of Machine Learning Research (JMLR), 6(Sep), 1453-1484.
10. Zhang, T., Damerau, F., & Johnson, D. (2002). Text chunking based on a generalization of winnow. J. Mach. Learn. Res., 2, 615-637.