Latent dirichlet allocation
Reads0
Chats0
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
More filters
Proceedings ArticleDOI
Joint latent topic models for text and citations
TL;DR: This work addresses the problem of joint modeling of text and citations in the topic modeling framework with two different models called the Pairwise-Link-LDA and the Link-PLSA-Lda models, which combine the LDA and PLSA models into a single graphical model.
Proceedings ArticleDOI
Predictive uncertainty estimation via prior networks
Andrey Malinin,Mark J. F. Gales +1 more
TL;DR: This work proposes a new framework for modeling predictive uncertainty called Prior Networks (PNs) which explicitly models distributional uncertainty by parameterizing a prior distribution over predictive distributions and evaluates PNs on the tasks of identifying out-of-distribution samples and detecting misclassification on the MNIST dataset, where they are found to outperform previous methods.
Journal ArticleDOI
BTM: Topic Modeling over Short Texts
TL;DR: This paper proposes a novel way for short text topic modeling, referred as biterm topic model (BTM), which learns topics by directly modeling the generation of word co-occurrence patterns in the corpus, making the inference effective with the rich corpus-level information.
Proceedings ArticleDOI
Neural Attentional Rating Regression with Review-level Explanations
TL;DR: A novel attention mechanism to explore the usefulness of reviews, and a Neural Attentional Regression model with Review-level Explanations (NARRE) for recommendation that consistently outperforms the state-of-the-art recommendation approaches in terms of rating prediction.
Posted Content
Learning To Generate Reviews and Discovering Sentiment
TL;DR: The properties of byte-level recurrent language models are explored and a single unit which performs sentiment analysis is found which achieves state of the art on the binary subset of the Stanford Sentiment Treebank.
References
More filters
Book
Bayesian Data Analysis
TL;DR: Detailed notes on Bayesian Computation Basics of Markov Chain Simulation, Regression Models, and Asymptotic Theorems are provided.
Journal ArticleDOI
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.
Book
Introduction to Modern Information Retrieval
Gerard Salton,Michael J. McGill +1 more
TL;DR: Reading is a need and a hobby at once and this condition is the on that will make you feel that you must read.
Book
Theory of probability
Harold Jeffreys,R. Bruce Lindsay +1 more
TL;DR: In this paper, the authors introduce the concept of direct probabilities, approximate methods and simplifications, and significant importance tests for various complications, including one new parameter, and various complications for frequency definitions and direct methods.