Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Maximum Entropy Markov Models for Information Extraction and Segmentation
ICML '00 Proceedings of the Seventeenth 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
Speech and Language Processing (2nd Edition)
Speech and Language Processing (2nd Edition)
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
Hi-index | 12.05 |
This work addresses natural language dialog planning for an intelligent web filtering model, which lets the user filter search results obtained by a traditional search engine and assists them to find what they really are looking for. Unlike state-of-the-art approaches, a stochastic planning model is proposed for a web-driven dialog system which uses Conditional Random Fields to predict next dialog moves. Experiments with real web users and different interaction settings show the promise of the approach to web-based adaptive planning aimed at information filtering.