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Finding High-Level Topics and Tweet Labeling Using Topic Models
2015 IEEE 21st International Conference on Parallel and Distributed Systems (ICPADS), 2015Making sense of Twitter data streams is challenging due to the extremely high volume of data. One way to address this challenge is to consider these data streams as containing a set of high-level topics. In this research we address the problem of: given a collection of tweets about K high-level topics, how to find topic words that describe these topics
Sameendra Samarawickrama +2 more
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Automatic labeling of multinomial topic models
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, 2007Multinomial distributions over words are frequently used to model topics in text collections. A common, major challenge in applying all such topic models to any text mining problem is to label a multinomial topic model accurately so that a user can interpret the discovered topic. So far, such labels have been generated manually in a subjective way.
Qiaozhu Mei +2 more
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Polynomial Topic Distribution with Topic Modeling for Generic Labeling
2019Topics generated by topic models are typically reproduced as a list of words. To decrease the cognitional overhead of understanding these topics for end-users, we have proposed labeling topics with a noun phrase that summarizes its theme or idea. Using the WordNet lexical database as candidate labels, we estimate natural labeling for documents with ...
Syeda Sumbul Hossain +2 more
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Topic Model Based Multi-Label Classification
2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI), 2016Multi-label classification is a common supervised machine learning problem where each instance is associated with multiple classes. The key challenge in this problem is learning the correlations between the classes. An additional challenge arises when the labels of the training instances are provided by noisy, heterogeneous crowd-workers with unknown ...
Divya Padmanabhan +3 more
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Latent Topic-Aware Multi-label Classification
2020In real-world applications, data are often associated with different labels. Although most extant multi-label learning algorithms consider the label correlations, they rarely consider the topic information hidden in the labels, where each topic is a group of related labels and different topics have different groups of labels.
Jianghong Ma, Yang Liu
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Off-Label Topical Calcineurin Inhibitor Use in Children
Pediatrics, 2013OBJECTIVE: To assess off-label use of the topical calcineurin inhibitors (TCIs), tacrolimus and pimecrolimus, in children during periods before and after regulatory action by the US Food and Drug Administration (FDA) in 2005.
Angelika D, Manthripragada +6 more
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Using Topic Labels for Text Summarization
2017Multi-document summarization is a difficult natural language processing task. Many extractive summarization methods consist of two steps: extract important concepts of documents and select sentences based on those concepts. In this paper, we introduce a method to use the Latent Dirichlet Allocation (LDA) topic labels as concepts, instead of n-gram or ...
Wanqiu Kou, Fang Li, Zhe Ye
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Topical questions regarding off-label use
DMW - Deutsche Medizinische Wochenschrift, 2003C, Dierks, G, Nitz
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