Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization
ACM Transactions on Mathematical Software (TOMS)
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Shallow parsing with conditional random fields
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Automatic Semantic Annotation of Polish Dialogue Corpus
TSD '08 Proceedings of the 11th international conference on Text, Speech and Dialogue
Biomedical named entity recognition using conditional random fields and rich feature sets
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
A hybrid Markov/semi-Markov conditional random field for sequence segmentation
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Time expressions ontology for information seeking dialogues in the public transport domain
IceTAL'10 Proceedings of the 7th international conference on Advances in natural language processing
Automatic semantic labeling of medical texts with feature structures
TSD'11 Proceedings of the 14th international conference on Text, speech and dialogue
Optimizing CRF-Based model for proper name recognition in polish texts
CICLing'12 Proceedings of the 13th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
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The article presents results of an experiment consisting in automatic concept annotation of the transliterated spontaneous human-human dialogues in the city transportation domain. The data source was a corpus of dialogues collected at a Warsaw call center and annotated with about 200 concepts' types. The machine learning technique we used is the linear-chain Conditional Random Fields (CRF) sequence labeling approach. The model based on word lemmas in a window of length 5 gave results of concept recognition with an F-measure equal to 0.85.