A probabilistic approach to solving crossword puzzles
Artificial Intelligence - Chips challenging champions: games, computers and Artificial Intelligence
Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Pruning Improves Heuristic Search for Cost-Sensitive Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Apprenticeship learning via inverse reinforcement learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Test-Cost Sensitive Naive Bayes Classification
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
The structure and performance of an open-domain question answering system
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Automatic Information Organization and Retrieval.
Automatic Information Organization and Retrieval.
Relating reinforcement learning performance to classification performance
ICML '05 Proceedings of the 22nd international conference on Machine learning
An Expected Utility Approach to Active Feature-Value Acquisition
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
NLTK: the Natural Language Toolkit
ETMTNLP '02 Proceedings of the ACL-02 Workshop on Effective tools and methodologies for teaching natural language processing and computational linguistics - Volume 1
Confidence estimation for machine translation
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Cost-sensitive feature acquisition and classification
Pattern Recognition
Handling Missing Values when Applying Classification Models
The Journal of Machine Learning Research
Sample-based learning and search with permanent and transient memories
Proceedings of the 25th international conference on Machine learning
Apprenticeship learning using linear programming
Proceedings of the 25th international conference on Machine learning
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
Algorithms for deterministic incremental dependency parsing
Computational Linguistics
Solving time-dependent planning problems
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Modeling perspective using adaptor grammars
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Ranking under temporal constraints
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Using classifier cascades for scalable e-mail classification
Proceedings of the 8th Annual Collaboration, Electronic messaging, Anti-Abuse and Spam Conference
An anytime algorithm for decision making under uncertainty
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Efficient Learning with Partially Observed Attributes
The Journal of Machine Learning Research
1 Billion Pages = 1 Million Dollars? mining the web to play "who wants to be a millionaire?"
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Model combination for event extraction in BioNLP 2011
BioNLP Shared Task '11 Proceedings of the BioNLP Shared Task 2011 Workshop
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Reconciliation of categorical opinions from multiple sources
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Cost-sensitive classification, where the features used in machine learning tasks have a cost, has been explored as a means of balancing knowledge against the expense of incrementally obtaining new features. We introduce a setting where humans engage in classification with incrementally revealed features: the collegiate trivia circuit. By providing the community with a web-based system to practice, we collected tens of thousands of implicit word-by-word ratings of how useful features are for eliciting correct answers. Observing humans' classification process, we improve the performance of a state-of-the art classifier. We also use the dataset to evaluate a system to compete in the incremental classification task through a reduction of reinforcement learning to classification. Our system learns when to answer a question, performing better than baselines and most human players.