A language modeling approach to information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
BIBE '01 Proceedings of the 2nd IEEE International Symposium on Bioinformatics and Bioengineering
Automatic image annotation and retrieval using cross-media relevance models
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
The Journal of Machine Learning Research
Understanding captions in biomedical publications
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Journal of VLSI Signal Processing Systems - Special issue on signal processing and neural networks for bioinformatics
High-recall protein entity recognition using a dictionary
Bioinformatics
Statistical entity-topic models
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Modeling the evolution of associated data
Data & Knowledge Engineering
Invited paper: Structured literature image finder: Parsing text and figures in biomedical literature
Web Semantics: Science, Services and Agents on the World Wide Web
A probabilistic topic-connection model for automatic image annotation
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Perspective hierarchical dirichlet process for user-tagged image modeling
Proceedings of the 20th ACM international conference on Information and knowledge management
ISMB/ECCB'09 Proceedings of the 2009 workshop of the BioLink Special Interest Group, international conference on Linking Literature, Information, and Knowledge for Biology
Discovering geographical topics in the twitter stream
Proceedings of the 21st international conference on World Wide Web
Automatic figure classification in bioscience literature
Journal of Biomedical Informatics
Practical collapsed variational bayes inference for hierarchical dirichlet process
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Community discovery and profiling with social messages
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Is that scene dangerous?: transferring knowledge over a video stream
Proceedings of the 5th Ph.D. workshop on Information and knowledge
Translating related words to videos and back through latent topics
Proceedings of the sixth ACM international conference on Web search and data mining
Scalable dynamic nonparametric Bayesian models of content and users
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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A major source of information (often the most crucial and informative part) in scholarly articles from scientific journals, proceedings and books are the figures that directly provide images and other graphical illustrations of key experimental results and other scientific contents. In biological articles, a typical figure often comprises multiple panels, accompanied by either scoped or global captioned text. Moreover, the text in the caption contains important semantic entities such as protein names, gene ontology, tissues labels, etc., relevant to the images in the figure. Due to the avalanche of biological literature in recent years, and increasing popularity of various bio-imaging techniques, automatic retrieval and summarization of biological information from literature figures has emerged as a major unsolved challenge in computational knowledge extraction and management in the life science. We present a new structured probabilistic topic model built on a realistic figure generation scheme to model the structurally annotated biological figures, and we derive an efficient inference algorithm based on collapsed Gibbs sampling for information retrieval and visualization. The resulting program constitutes one of the key IR engines in our SLIF system that has recently entered the final round (4 out 70 competing systems) of the Elsevier Grand Challenge on Knowledge Enhancement in the Life Science. Here we present various evaluations on a number of data mining tasks to illustrate our method.