A language modeling approach to information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Improving the effectiveness of information retrieval with local context analysis
ACM Transactions on Information Systems (TOIS)
Re-ranking model based on document clusters
Information Processing and Management: an International Journal
Model-based feedback in the language modeling approach to information retrieval
Proceedings of the tenth international conference on Information and knowledge management
Modern Information Retrieval
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
The Journal of Machine Learning Research
A study of smoothing methods for language models applied to information retrieval
ACM Transactions on Information Systems (TOIS)
Retrieval evaluation with incomplete information
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
PageRank without hyperlinks: structural re-ranking using links induced by language models
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Improving web search results using affinity graph
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Re-ranking method based on inter-document distances
Information Processing and Management: an International Journal
Regularizing ad hoc retrieval scores
Proceedings of the 14th ACM international conference on Information and knowledge management
Respect my authority!: HITS without hyperlinks, utilizing cluster-based language models
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
LDA-based document models for ad-hoc retrieval
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Topic sentiment mixture: modeling facets and opinions in weblogs
Proceedings of the 16th international conference on World Wide Web
Proceedings of the 17th international conference on World Wide Web
Modeling online reviews with multi-grain topic models
Proceedings of the 17th international conference on World Wide Web
Effective latent space graph-based re-ranking model with global consistency
Proceedings of the Second ACM International Conference on Web Search and Data Mining
A late fusion approach to cross-lingual document re-ranking
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Smoothing methods and cross-language document re-ranking
CLEF'09 Proceedings of the 10th cross-language evaluation forum conference on Multilingual information access evaluation: text retrieval experiments
Dual-space re-ranking model for document retrieval
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
A study on query expansion based on topic distributions of retrieved documents
CICLing'13 Proceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume 2
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The problem of re-ranking initial retrieval results exploring the intrinsic structure of documents is widely researched in information retrieval (IR) and has attracted a considerable amount of time and study. However, one of the drawbacks is that those algorithms treat queries and documents separately. Furthermore, most of the approaches are predominantly built upon graph-based methods, which may ignore some hidden information among the retrieval set. This paper proposes a novel document re-ranking method based on Latent Dirichlet Allocation (LDA) which exploits the implicit structure of the documents with respect to original queries. Rather than relying on graph-based techniques to identify the internal structure, the approach tries to find the latent structure of "topics" or "concepts" in the initial re-trieval set. Then we compute the distance between queries and initial retrieval results based on latent semantic information deduced. Empirical results demonstrate that the method can comfortably achieve significant improvement over various baseline systems.