Latent dirichlet allocation
TLDR
This work proposes a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams, and Hofmann's aspect model.Abstract:
We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections of discrete data such as text corpora. LDA is a three-level hierarchical Bayesian model, in which each item of a collection is modeled as a finite mixture over an underlying set of topics. Each topic is, in turn, modeled as an infinite mixture over an underlying set of topic probabilities. In the context of text modeling, the topic probabilities provide an explicit representation of a document. We present efficient approximate inference techniques based on variational methods and an EM algorithm for empirical Bayes parameter estimation. We report results in document modeling, text classification, and collaborative filtering, comparing to a mixture of unigrams model and the probabilistic LSI model.read more
Citations
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Proceedings ArticleDOI
A Bayesian Mixed Effects Model of Literary Character
TL;DR: A model that employs multiple effects to account for the influence of extra-linguistic information (such as author) is introduced and it is found that this method leads to improved agreement with the preregistered judgments of a literary scholar, complementing the results of alternative models.
Proceedings Article
Supervised coupled dictionary learning with group structures for multi-modal retrieval
TL;DR: This paper introduces coupled dictionary learning (DL) into supervised sparse coding for multi-modal (crossmedia) retrieval with group structures for Multi-Modal retrieval (SliM2), and formulates the multimodal mapping as a constrained dictionary learning problem.
Journal ArticleDOI
Representation learning for very short texts using weighted word embedding aggregation
TL;DR: A weight-based model and a learning procedure based on a novel median-based loss function designed to mitigate the negative effect of outliers are designed and found that the method outperforms the baseline approaches in the experiments, and that it generalizes well on different word embeddings without retraining.
Journal ArticleDOI
Multi-label learning: a review of the state of the art and ongoing research
TL;DR: The formal definition of the paradigm, the analysis of its impact on the literature, its main applications, works developed, pitfalls and guidelines, and ongoing research are presented.
Journal ArticleDOI
Leveraging Data Science to Combat COVID-19: A Comprehensive Review
Siddique Latif,Muhammad Usman,Sanaullah Manzoor,Waleed Iqbal,Junaid Qadir,Gareth Tyson,Ignacio Castro,Adeel Razi,Maged N. Kamel Boulos,Adrian Weller,Jon Crowcroft +10 more
TL;DR: This paper attempts to systematise the various COVID-19 research activities leveraging data science, where data science is defined broadly to encompass the various methods and tools that can be used to store, process, and extract insights from data.
References
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Indexing by Latent Semantic Analysis
TL;DR: A new method for automatic indexing and retrieval to take advantage of implicit higher-order structure in the association of terms with documents (“semantic structure”) in order to improve the detection of relevant documents on the basis of terms found in queries.
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Gerard Salton,Michael J. McGill +1 more
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Harold Jeffreys,R. Bruce Lindsay +1 more
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