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 Chaitanya Chandra Chemudugunta

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Bibliometrics: publication history
Average citations per article21.00
Citation Count168
Publication count8
Publication years2006-2010
Available for download4
Average downloads per article1,022.00
Downloads (cumulative)4,088
Downloads (12 Months)215
Downloads (6 Weeks)27

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1 published by ACM
January 2010 ACM Transactions on Information Systems (TOIS): Volume 28 Issue 1, January 2010
Publisher: ACM
Citation Count: 48
Downloads (6 Weeks): 17,   Downloads (12 Months): 130,   Downloads (Overall): 1,818

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We propose an unsupervised learning technique for extracting information about authors and topics from large text collections. We model documents as if they were generated by a two-stage stochastic process. An author is represented by a probability distribution over topics, and each topic is represented as a probability distribution over ...
Keywords: Gibbs sampling, author models, unsupervised learning, perplexity, Topic models

January 2009
Citation Count: 0

Statistical topic models are a class of probabilistic latent variable models for textual data that represent text documents as distributions over topics. These models have been shown to produce interpretable summarization of documents in the form of topics. In this dissertation, we investigate how the statistical topic modeling ...

3 published by ACM
October 2008 CIKM '08: Proceedings of the 17th ACM conference on Information and knowledge management
Publisher: ACM
Citation Count: 9
Downloads (6 Weeks): 5,   Downloads (12 Months): 22,   Downloads (Overall): 497

Full text available: PDFPDF
Statistical topic models provide a general data-driven framework for automated discovery of high-level knowledge from large collections of text documents. While topic models can potentially discover a broad range of themes in a data set, the interpretability of the learned topics is not always ideal. Human-defined concepts, on the other ...
Keywords: semantic concepts, statistical topic models, ontologies, unsupervised learning

October 2008 ISWC '08: Proceedings of the 7th International Conference on The Semantic Web
Publisher: Springer-Verlag
Citation Count: 19

Human-defined concepts are fundamental building-blocks in constructing knowledge bases such as ontologies. Statistical learning techniques provide an alternative automated approach to concept definition, driven by data rather than prior knowledge. In this paper we propose a probabilistic modeling framework that combines both human-defined concepts and data-driven topics in a principled ...
Keywords: tagging, ontologies, topic models, unsupervised learning

5 published by ACM
June 2007 JCDL '07: Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries
Publisher: ACM
Citation Count: 12
Downloads (6 Weeks): 2,   Downloads (12 Months): 18,   Downloads (Overall): 671

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Creating a collection of metadata records from disparate and diverse sources often results in uneven, unreliable and variable quality subject metadata. Having uniform, consistent and enriched subject metadata allows users to more easily discover material, browse the collection, and limit keyword search results by subject. We demonstrate how statistical topic ...
Keywords: metadata enhancement, topic model, OAI, digital libraries, metadata enrichment, browsing, clustering

December 2006 NIPS'06: Proceedings of the 19th International Conference on Neural Information Processing Systems
Publisher: MIT Press
Citation Count: 6

Techniques such as probabilistic topic models and latent-semantic indexing have been shown to be broadly useful at automatically extracting the topical or semantic content of documents, or more generally for dimension-reduction of sparse count data. These types of models and algorithms can be viewed as generating an abstraction from the ...

7 published by ACM
August 2006 KDD '06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Publisher: ACM
Citation Count: 52
Downloads (6 Weeks): 4,   Downloads (12 Months): 46,   Downloads (Overall): 1,103

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The primary purpose of news articles is to convey information about who, what, when and where. But learning and summarizing these relationships for collections of thousands to millions of articles is difficult. While statistical topic models have been highly successful at topically summarizing huge collections of text documents, they do ...
Keywords: entity recognition, text modeling, topic modeling

May 2006 ISI'06: Proceedings of the 4th IEEE international conference on Intelligence and Security Informatics
Publisher: Springer-Verlag
Citation Count: 21

Statistical language models can learn relationships between topics discussed in a document collection and persons, organizations and places mentioned in each document. We present a novel combination of statistical topic models and named-entity recognizers to jointly analyze entities mentioned (persons, organizations and places) and topics discussed in a collection of ...

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