A language modeling approach to information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Cross-lingual relevance models
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
The Journal of Machine Learning Research
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
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
Evaluation methods for topic models
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Cross-language linking of news stories on the web using interlingual topic modelling
Proceedings of the 2nd ACM workshop on Social web search and mining
Explicit versus latent concept models for cross-language information retrieval
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
Multilingual topic models for unaligned text
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Cross-lingual latent topic extraction
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Cross lingual text classification by mining multilingual topics from wikipedia
Proceedings of the fourth ACM international conference on Web search and data mining
Identifying word translations from comparable corpora using latent topic models
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Knowledge transfer across multilingual corpora via latent topics
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part I
Extracting multilingual topics from unaligned comparable corpora
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
Combining wikipedia-based concept models for cross-language retrieval
IRFC'10 Proceedings of the First international Information Retrieval Facility conference on Adbances in Multidisciplinary Retrieval
Cross-language information retrieval with latent topic models trained on a comparable corpus
AIRS'11 Proceedings of the 7th Asia conference on Information Retrieval Technology
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting highly confident word translations from comparable corpora without any prior knowledge
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
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Probabilistic topic models are a group of unsupervised generative machine learning models that can be effectively trained on large text collections. They model document content as a two-step generation process, i.e., documents are observed as mixtures of latent topics, while topics are probability distributions over vocabulary words. Recently, a significant research effort has been invested into transferring the probabilistic topic modeling concept from monolingual to multilingual settings. Novel topic models have been designed to work with parallel and comparable multilingual data (e.g., Wikipedia or news data discussing the same events). Probabilistic topics models offer an elegant way to represent content across different languages. Their probabilistic framework allows for their easy integration into a language modeling framework for monolingual and cross-lingual information retrieval. Moreover, we present how to use the knowledge from the topic models in the tasks of cross-lingual event clustering, cross-lingual document classification and the detection of cross-lingual semantic similarity of words. The tutorial also demonstrates how semantically similar words across languages are integrated as useful additional evidences in cross-lingual information retrieval models.