Using statistical testing in the evaluation of retrieval experiments
SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
A language modeling approach to information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Making large-scale support vector machine learning practical
Advances in kernel methods
A hidden Markov model information retrieval system
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
A study of smoothing methods for language models applied to information retrieval
ACM Transactions on Information Systems (TOIS)
Linear discriminant model for information retrieval
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
TREC: Experiment and Evaluation in Information Retrieval (Digital Libraries and Electronic Publishing)
Adapting ranking SVM to document retrieval
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
A support vector method for optimizing average precision
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Adaptive Bayesian Latent Semantic Analysis
IEEE Transactions on Audio, Speech, and Language Processing
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Statistical language modeling has been successfully developed for speech recognition and information retrieval. The minimum classification error (MCE) training was undertaken to enhance speech recognition performance by minimizing the word error rate. This paper presents a new minimum rank error (MRE) algorithm for n-gram language model training. Rather than speech recognition, the proposed language models are estimated for information retrieval by considering the metric of average precision. However, the maximization of average precision is closely linked to minimizing the rank error or optimizing the order of the ranked documents. Accordingly, this paper calculates the rank error loss function from the misordering pairs of relevant and irrelevant documents in the rank list. The Bayes risk due to the expected rank loss is minimized to develop the Bayesian retrieval rule for ad-hoc information retrieval. Consequently, the discriminative training of language model is performed by integrating discrimination information from individual relevant documents relative to their corresponding irrelevant documents. Experimental results on TREC collections indicate that the proposed MRE language model improves the order of relevant documents, and degrades that of irrelevant documents. The MRE method achieves significantly higher average precision for test queries than the maximum likelihood and the MCE retrieval models.