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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Journal ArticleDOI
Wearable activity trackers, accuracy, adoption, acceptance and health impact: A systematic literature review.
Grace Shin,Mohammad Hossein Jarrahi,Yu Fei,Amir Karami,Nicci Gafinowitz,Ahjung Byun,Xiaopeng Lu +6 more
TL;DR: An interdisciplinary approach to wearable activity trackers is taken to attempt to understand the rich human-information interaction that is enabled by WAT adoption, and to propose several new research questions.
Proceedings Article
Learning Effective and Interpretable Semantic Models using Non-Negative Sparse Embedding
TL;DR: It is found that word representations learned by Non-Negative Sparse Embedding (NNSE), a variant of matrix factorization, are sparse, effective, and highly interpretable, the first approach which yields semantic representation of words satisfying these three desirable properties.
Journal ArticleDOI
Financial reporting fraud and other forms of misconduct: a multidisciplinary review of the literature
TL;DR: This article reviewed the literature on financial reporting misconduct from the perspectives of law, accounting, and finance, and established a common language for researchers interested in this line of research, described the main findings and challenges in these literatures, and provided directions for future research.
Proceedings Article
A Latent Dirichlet Allocation Method for Selectional Preferences
Alan Ritter,Oren Etzioni +1 more
TL;DR: LDA-SP, which utilizes LinkLDA to model selectional preferences, combines the benefits of previous approaches: like traditional class-based approaches, it produces human-interpretable classes describing each relation's preferences, but it is competitive with non-class-based methods in predictive power.
Proceedings Article
The IBP Compound Dirichlet Process and its Application to Focused Topic Modeling
TL;DR: The IBP compound Dirichlet process (ICD) is developed, a Bayesian nonparametric prior that decouples across-data prevalence and within-data proportion in a mixed membership model and shows superior performance over the HDP-based topic model.
References
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