Faceted metadata for image search and browsing
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The Journal of Machine Learning Research
Combining the language model and inference network approaches to retrieval
Information Processing and Management: an International Journal - Special issue: Bayesian networks and information retrieval
Automatic construction of multifaceted browsing interfaces
Proceedings of the 14th ACM international conference on Information and knowledge management
TREC: Experiment and Evaluation in Information Retrieval (Digital Libraries and Electronic Publishing)
Pachinko allocation: DAG-structured mixture models of topic correlations
ICML '06 Proceedings of the 23rd international conference on Machine learning
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
Examining the effectiveness of real-time query expansion
Information Processing and Management: an International Journal
Organizing the OCA: learning faceted subjects from a library of digital books
Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries
Personalized interactive faceted search
Proceedings of the 17th international conference on World Wide Web
Mining multi-faceted overviews of arbitrary topics in a text collection
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Modeling Documents by Combining Semantic Concepts with Unsupervised Statistical Learning
ISWC '08 Proceedings of the 7th International Conference on The Semantic Web
Search Engines: Information Retrieval in Practice
Search Engines: Information Retrieval in Practice
A Comparative Study of Utilizing Topic Models for Information Retrieval
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
Incorporating domain knowledge into topic modeling via Dirichlet Forest priors
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
The Sensitivity of Latent Dirichlet Allocation for Information Retrieval
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Evaluating topic models for digital libraries
Proceedings of the 10th annual joint conference on Digital libraries
Interactive retrieval based on faceted feedback
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Learning to rank for information retrieval
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Automatic evaluation of topic coherence
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
An architecture for parallel topic models
Proceedings of the VLDB Endowment
Best topic word selection for topic labelling
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Expectation-propagation for the generative aspect model
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Practical collapsed variational bayes inference for hierarchical dirichlet process
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Incorporating statistical topic information in relevance feedback
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Exploring topic coherence over many models and many topics
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Unsupervised latent concept modeling to identify query facets
Proceedings of the 10th Conference on Open Research Areas in Information Retrieval
Efficient Nearest-Neighbor Search in the Probability Simplex
Proceedings of the 2013 Conference on the Theory of Information Retrieval
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We consider the problem of a user navigating an unfamiliar corpus of text documents where document metadata is limited or unavailable, the domain is specialized, and the user base is small. These challenging conditions may hold, for example, within an organization such as a business or government agency. We propose to augment standard keyword search with user feedback on latent topics. These topics are automatically learned from the corpus in an unsupervised manner and presented alongside search results. User feedback is then used to reformulate the original query, resulting in improved information retrieval performance in our experiments.