Latent dirichlet allocation
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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
Predicting Human Brain Activity Associated with the Meanings of Nouns
Tom M. Mitchell,Svetlana V. Shinkareva,Andrew Carlson,Kai-Min Chang,Vicente L. Malave,Robert A. Mason,Marcel Adam Just +6 more
TL;DR: A computational model is presented that predicts the functional magnetic resonance imaging (fMRI) neural activation associated with words for which fMRI data are not yet available, trained with a combination of data from a trillion-word text corpus and observed f MRI data associated with viewing several dozen concrete nouns.
Proceedings ArticleDOI
Modeling annotated data
David M. Blei,Michael I. Jordan +1 more
TL;DR: Three hierarchical probabilistic mixture models which aim to describe annotated data with multiple types, culminating in correspondence latent Dirichlet allocation, a latent variable model that is effective at modeling the joint distribution of both types and the conditional distribution of the annotation given the primary type.
Book ChapterDOI
Comparing twitter and traditional media using topic models
TL;DR: This paper empirically compare the content of Twitter with a traditional news medium, New York Times, using unsupervised topic modeling, and finds interesting and useful findings for downstream IR or DM applications.
Book ChapterDOI
Probabilistic Topic Models
TL;DR: Landauer and Dumais as discussed by the authors showed that applying a statistical method such as latent semantic analysis (LSA) to large databases can yield insight into human cognition, and proposed a class of statistical models in which the semantic properties of words and documents are expressed in terms of probabilistic topics.
Proceedings ArticleDOI
Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification
TL;DR: Three neural networks are developed to effectively incorporate the supervision from sentiment polarity of text (e.g. sentences or tweets) in their loss functions and the performance of SSWE is improved by concatenating SSWE with existing feature set.
References
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Indexing by Latent Semantic Analysis
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Harold Jeffreys,R. Bruce Lindsay +1 more
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