Results 41 to 50 of about 144,717 (245)
CoRR, 2010 The syntactic topic model (STM) is a Bayesian nonparametric model of language that discovers latent distributions of words (topics) that are both semantically and syntactically coherent. The STM models dependency parsed corpora where sentences are grouped into documents.
Jordan L. Boyd-Graber, David M. Blei
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Polylingual topic models [PDF]
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing Volume 2 - EMNLP '09, 2009 Topic models are a useful tool for analyzing large text collections, but have previously been applied in only monolingual, or at most bilingual, contexts. Meanwhile, massive collections of interlinked documents in dozens of languages, such as Wikipedia, are now widely available, calling for tools that can characterize content in many languages.
Mimno, David +4 more
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Social Sciences, 2018 The Men’s Rights Activism (MRA) movement and its sub-movement The Red Pill (TRP), has flourished online, offering support and advice to men who feel their masculinity is being challenged by societal shifts.
J. B. Mountford
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On a Topic Model for Sentences
Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, 2016 Probabilistic topic models are generative models that describe the content of documents by discovering the latent topics underlying them. However, the structure of the textual input, and for instance the grouping of words in coherent text spans such as sentences, contains much information which is generally lost with these models.
Georgios Balikas +2 more
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, 2010 We introduce supervised latent Dirichlet allocation (sLDA), a statistical model of labelled documents. The model accommodates a variety of response types. We derive an approximate maximum-likelihood procedure for parameter estimation, which relies on variational methods to handle intractable posterior expectations.
David M. Blei, Jon D. McAuliffe
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Differential Topic Models [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015 In applications we may want to compare different document collections: they could have shared content but also different and unique aspects in particular collections. This task has been called comparative text mining or cross-collection modeling.
Changyou Chen +4 more
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Pediatric Blood &Cancer, EarlyView.ABSTRACT Background Central nervous system (CNS) involvement in childhood acute lymphoblastic leukemia (ALL) is assessed by cell counting and cytomorphology from cerebrospinal fluid (CSF) and is used for treatment stratification worldwide. The ratio of “CNS2” patients in clinical trials ranges from 3% to 40%, with unclear prognostic significance ...
Laura Almási +14 more
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Neurocomputing, 2023 Nonnegative matrix factorization (NMF) based topic modeling methods do not rely on model- or data-assumptions much. However, they are usually formulated as difficult optimization problems, which may suffer from bad local minima and high computational complexity.
Jianyu Wang, Xiao-Lei Zhang 0001
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Systematic Literature Review of Topic Labeling
IEEE AccessThe rapid growth of textual data on the web has led researchers to develop methods in Natural Language Processing (NLP) to process, understand, and identify topics.
Salma Mekaoui +3 more
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