The nature of statistical learning theory
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An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
The Perceptron Algorithm with Uneven Margins
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ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Discriminative Reranking for Natural Language Parsing
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Constraint Classification: A New Approach to Multiclass Classification
ALT '02 Proceedings of the 13th International Conference on Algorithmic Learning Theory
Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
A family of additive online algorithms for category ranking
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A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
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COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
BLEU: a method for automatic evaluation of machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Minimum error rate training in statistical machine translation
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
An SVM based voting algorithm with application to parse reranking
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
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Flexible margin selection for reranking with full pairwise samples
IJCNLP'04 Proceedings of the First international joint conference on Natural Language Processing
High accuracy retrieval with multiple nested ranker
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Learning-based summarisation of XML documents
Information Retrieval
Fast learning of document ranking functions with the committee perceptron
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
The unimodal model for the classification of ordinal data
Neural Networks
Online learning from click data for sponsored search
Proceedings of the 17th international conference on World Wide Web
Learning to Predict One or More Ranks in Ordinal Regression Tasks
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Knowledge and Information Systems
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Learning dense models of query similarity from user click logs
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Learning to link entities with knowledge base
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
ACM Transactions on Information Systems (TOIS)
Learning to rank for why-question answering
Information Retrieval
Improved answer ranking in social question-answering portals
Proceedings of the 3rd international workshop on Search and mining user-generated contents
Automatic text summarization based on word-clusters and ranking algorithms
ECIR'05 Proceedings of the 27th European conference on Advances in Information Retrieval Research
Say Anything: Using Textual Case-Based Reasoning to Enable Open-Domain Interactive Storytelling
ACM Transactions on Interactive Intelligent Systems (TiiS) - Special Issue on Common Sense for Interactive Systems
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
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This work is inspired by the so-called reranking tasks in natural language processing. In this paper, we first study the ranking, reranking, and ordinal regression algorithms proposed recently in the context of ranks and margins. Then we propose a general framework for ranking and reranking, and introduce a series of variants of the perceptron algorithm for ranking and reranking in the new framework. Compared to the approach of using pairwise objects as training samples, the new algorithms reduces the data complexity and training time. We apply the new perceptron algorithms to the parse reranking and machine translation reranking tasks, and study the performance of reranking by employing various definitions of the margins.