A model for reasoning about persistence and causation
Computational Intelligence
Original Contribution: Stacked generalization
Neural Networks
Some advances in transformation-based part of speech tagging
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
A maximum entropy approach to natural language processing
Computational Linguistics
Factorial Hidden Markov Models
Machine Learning - Special issue on learning with probabilistic representations
Learning Information Extraction Rules for Semi-Structured and Free Text
Machine Learning - Special issue on natural language learning
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Relational learning of pattern-match rules for information extraction
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
The Hierarchical Hidden Markov Model: Analysis and Applications
Machine Learning
Training products of experts by minimizing contrastive divergence
Neural Computation
Factorial Markov Random Fields
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
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
Table extraction using conditional random fields
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Machine learning for information extraction in informal domains
Machine learning for information extraction in informal domains
A family of algorithms for approximate bayesian inference
A family of algorithms for approximate bayesian inference
Stochastic processes on graphs with cycles: geometric and variational approaches
Stochastic processes on graphs with cycles: geometric and variational approaches
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Chunking with support vector machines
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
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
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Introduction to the CoNLL-2000 shared task: chunking
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
A comparison of algorithms for maximum entropy parameter estimation
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
Accelerated training of conditional random fields with stochastic gradient methods
ICML '06 Proceedings of the 23rd international conference on Machine learning
Online large-margin training of dependency parsers
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Composition of conditional random fields for transfer learning
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Policy recognition in the abstract hidden Markov model
Journal of Artificial Intelligence Research
Bayesian information extraction network
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Hierarchical hidden Markov models for information extraction
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Adaptive information extraction from text by rule induction and generalisation
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Relational learning via propositional algorithms: an information extraction case study
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Multiscale conditional random fields for image labeling
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Discriminative probabilistic models for relational data
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Constructing free-energy approximations and generalized belief propagation algorithms
IEEE Transactions on Information Theory
A unified architecture for natural language processing: deep neural networks with multitask learning
Proceedings of the 25th international conference on Machine learning
Real world activity recognition with multiple goals
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
An algorithm for analyzing personalized online commercial intention
Proceedings of the 2nd International Workshop on Data Mining and Audience Intelligence for Advertising
Keyword query cleaning using hidden Markov models
Proceedings of the First International Workshop on Keyword Search on Structured Data
Learning-based named entity recognition for morphologically-rich, resource-scarce languages
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
CIGAR: concurrent and interleaving goal and activity recognition
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Feature selection for activity recognition in multi-robot domains
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Gesture salience as a hidden variable for coreference resolution and keyframe extraction
Journal of Artificial Intelligence Research
Review: The use of pervasive sensing for behaviour profiling - a survey
Pervasive and Mobile Computing
Probabilistic models for concurrent chatting activity recognition
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Selecting features of linear-chain conditional random fields via greedy stage-wise algorithms
Pattern Recognition Letters
Piecewise training for structured prediction
Machine Learning
Using Conditional Random Fields for Decision-Theoretic Planning
MDAI '09 Proceedings of the 6th International Conference on Modeling Decisions for Artificial Intelligence
A Study of Parts-Based Object Class Detection Using Complete Graphs
International Journal of Computer Vision
Object relevance weight pattern mining for activity recognition and segmentation
Pervasive and Mobile Computing
Real-time activity classification using ambient and wearable sensors
IEEE Transactions on Information Technology in Biomedicine - Special section on body sensor networks
CarpeDiem: Optimizing the Viterbi Algorithm and Applications to Supervised Sequential Learning
The Journal of Machine Learning Research
A discriminative model corresponding to hierarchical HMMs
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Sports video segmentation using a hierarchical hidden CRF
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
Advances in view-invariant human motion analysis: a review
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Decision detection using hierarchical graphical models
ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
Turbo parsers: dependency parsing by approximate variational inference
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Better punctuation prediction with dynamic conditional random fields
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Natural language querying over databases using cascaded CRFs
ADBIS'10 Proceedings of the 14th east European conference on Advances in databases and information systems
Probabilistic models for concurrent chatting activity recognition
ACM Transactions on Intelligent Systems and Technology (TIST)
Learning the behavior model of a robot
Autonomous Robots
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Multi-dimensional classification with Bayesian networks
International Journal of Approximate Reasoning
Recognizing multi-user activities using wearable sensors in a smart home
Pervasive and Mobile Computing
Language models as representations for weakly-supervised NLP tasks
CoNLL '11 Proceedings of the Fifteenth Conference on Computational Natural Language Learning
Towards a top-down and bottom-up bidirectional approach to joint information extraction
Proceedings of the 20th ACM international conference on Information and knowledge management
Natural Language Processing (Almost) from Scratch
The Journal of Machine Learning Research
Self-supervised capturing of users' activities from weblogs
International Journal of Intelligent Information and Database Systems
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Energy-Aware agents for detecting nonessential appliances
PRIMA'10 Proceedings of the 13th international conference on Principles and Practice of Multi-Agent Systems
PROBABILISTIC MODELS FOR FOCUSED WEB CRAWLING
Computational Intelligence
Minimum-risk training of approximate CRF-based NLP systems
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
A novel discriminative framework for sentence-level discourse analysis
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Joint bilingual name tagging for parallel corpora
Proceedings of the 21st ACM international conference on Information and knowledge management
An inference-based model of word meaning in context as a paraphrase distribution
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Sections on Paraphrasing; Intelligent Systems for Socially Aware Computing; Social Computing, Behavioral-Cultural Modeling, and Prediction
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In sequence modeling, we often wish to represent complex interaction between labels, such as when performing multiple, cascaded labeling tasks on the same sequence, or when long-range dependencies exist. We present dynamic conditional random fields (DCRFs), a generalization of linear-chain conditional random fields (CRFs) in which each time slice contains a set of state variables and edges---a distributed state representation as in dynamic Bayesian networks (DBNs)---and parameters are tied across slices. Since exact inference can be intractable in such models, we perform approximate inference using several schedules for belief propagation, including tree-based reparameterization (TRP). On a natural-language chunking task, we show that a DCRF performs better than a series of linear-chain CRFs, achieving comparable performance using only half the training data. In addition to maximum conditional likelihood, we present two alternative approaches for training DCRFs: marginal likelihood training, for when we are primarily interested in predicting only a subset of the variables, and cascaded training, for when we have a distinct data set for each state variable, as in transfer learning. We evaluate marginal training and cascaded training on both synthetic data and real-world text data, finding that marginal training can improve accuracy when uncertainty exists over the latent variables, and that for transfer learning, a DCRF trained in a cascaded fashion performs better than a linear-chain CRF that predicts the final task directly.