Two-stage language models for information retrieval
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
A study of smoothing methods for language models applied to information retrieval
ACM Transactions on Information Systems (TOIS)
Dependence language model for information retrieval
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Linear discriminant model for information retrieval
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
A Markov random field model for term dependencies
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Optimisation methods for ranking functions with multiple parameters
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Sentence-level contextual opinion retrieval
Proceedings of the 20th international conference companion on World wide web
Unsupervised query segmentation using clickthrough for information retrieval
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Using predicate-argument structures for context-dependent opinion retrieval
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part II
Semantic-based opinion retrieval using predicate-argument structures and subjective adjectives
AIRS'11 Proceedings of the 7th Asia conference on Information Retrieval Technology
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Although higher order language models (LMs) have shown benefit of capturing word dependencies for Information retrieval(IR), the tuning of the increased number of free parameters remains a formidable engineering challenge. Consequently,in many real world retrieval systems, applying higher order LMs is an exception rather than the rule. In this study, we address the parameter tuning problem using a framework based on a linear ranking model in which different component models are incorporated as features. Using unigram and bigram LMs with 2 stage smoothing as examples, we show that our method leads to a bigram LM that outperforms significantly its unigram counterpart and the well-tuned BM25 model.