On the limited memory BFGS method for large scale optimization
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A maximum entropy approach to natural language processing
Computational Linguistics
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
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Grouper: a dynamic clustering interface to Web search results
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Text Classification from Labeled and Unlabeled Documents using EM
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Distributional word clusters vs. words for text categorization
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Latent semantic models for collaborative filtering
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Learning to cluster web search results
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Using the web to overcome data sparseness
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Computing semantic relatedness using Wikipedia-based explicit semantic analysis
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Similarity measures for short segments of text
ECIR'07 Proceedings of the 29th European conference on IR research
Expectation-propagation for the generative aspect model
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Matching and Ranking with Hidden Topics towards Online Contextual Advertising
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Exploiting Wikipedia as external knowledge for document clustering
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Web Search Clustering and Labeling with Hidden Topics
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Exploiting internal and external semantics for the clustering of short texts using world knowledge
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Framework for timely and accurate ads on mobile devices
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Cross-cultural analysis of blogs and forums with mixed-collection topic models
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
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Web opinions analysis with scalable distance-basedclustering
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Short text classification in twitter to improve information filtering
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LDA for on-the-fly auto tagging
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Collaboration analytics: mining work patterns from collaboration activities
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Cross lingual text classification by mining multilingual topics from wikipedia
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Empirical study of topic modeling in Twitter
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Query by document via a decomposition-based two-level retrieval approach
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Entity disambiguation with hierarchical topic models
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Discovering context: classifying tweets through a semantic transform based on wikipedia
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IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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This paper presents a general framework for building classifiers that deal with short and sparse text & Web segments by making the most of hidden topics discovered from large-scale data collections. The main motivation of this work is that many classification tasks working with short segments of text & Web, such as search snippets, forum & chat messages, blog & news feeds, product reviews, and book & movie summaries, fail to achieve high accuracy due to the data sparseness. We, therefore, come up with an idea of gaining external knowledge to make the data more related as well as expand the coverage of classifiers to handle future data better. The underlying idea of the framework is that for each classification task, we collect a large-scale external data collection called "universal dataset", and then build a classifier on both a (small) set of labeled training data and a rich set of hidden topics discovered from that data collection. The framework is general enough to be applied to different data domains and genres ranging from Web search results to medical text. We did a careful evaluation on several hundred megabytes of Wikipedia (30M words) and MEDLINE (18M words) with two tasks: "Web search domain disambiguation" and "disease categorization for medical text", and achieved significant quality enhancement.