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 Article
Optimizing Semantic Coherence in Topic Models
TL;DR: A novel statistical topic model based on an automated evaluation metric based on this metric that significantly improves topic quality in a large-scale document collection from the National Institutes of Health (NIH).
Posted Content
Deep Sets
Manzil Zaheer,Satwik Kottur,Siamak Ravanbakhsh,Barnabás Póczos,Ruslan Salakhutdinov,Alexander J. Smola +5 more
TL;DR: The main theorem characterizes the permutation invariant objective functions and provides a family of functions to which any permutation covariant objective function must belong, which enables the design of a deep network architecture that can operate on sets and which can be deployed on a variety of scenarios including both unsupervised and supervised learning tasks.
Proceedings ArticleDOI
Topics over time: a non-Markov continuous-time model of topical trends
Xuerui Wang,Andrew McCallum +1 more
TL;DR: An LDA-style topic model is presented that captures not only the low-dimensional structure of data, but also how the structure changes over time, showing improved topics, better timestamp prediction, and interpretable trends.
Proceedings Article
Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions
TL;DR: A novel machine learning framework based on recursive autoencoders for sentence-level prediction of sentiment label distributions that outperform other state-of-the-art approaches on commonly used datasets, without using any pre-defined sentiment lexica or polarity shifting rules.
Book ChapterDOI
A Survey of Opinion Mining and Sentiment Analysis
TL;DR: Sentiment analysis or opinion mining is the computational study of people’s opinions, appraisals, attitudes, and emotions toward entities, individuals, issues, events, topics and their attributes.
References
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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.
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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.