Topic
Latent Dirichlet allocation
About: Latent Dirichlet allocation is a research topic. Over the lifetime, 5351 publications have been published within this topic receiving 212555 citations. The topic is also known as: LDA.
Papers published on a yearly basis
Papers
More filters
••
TL;DR: A method of adaptively selecting the best LDA model based on density is proposed, and experiments show that the proposed method can achieve performance matching the best of LDA without manually tuning the number of topics.
497 citations
••
21 Jun 2010TL;DR: A measure to identify the correct number of topics in mechanisms like Latent Dirichlet Allocation and offer empirical evidence in its favor in terms of classification accuracy and the number of Topics that are naturally present in the corpus is proposed.
Abstract: It is important to identify the “correct” number of topics in mechanisms like Latent Dirichlet Allocation(LDA) as they determine the quality of features that are presented as features for classifiers like SVM. In this work we propose a measure to identify the correct number of topics and offer empirical evidence in its favor in terms of classification accuracy and the number of topics that are naturally present in the corpus. We show the merit of the measure by applying it on real-world as well as synthetic data sets(both text and images). In proposing this measure, we view LDA as a matrix factorization mechanism, wherein a given corpus C is split into two matrix factors M1 and M2 as given by Cd*w = M1d*t x Qt*w. Where d is the number of documents present in the corpus and w is the size of the vocabulary. The quality of the split depends on “t”, the right number of topics chosen. The measure is computed in terms of symmetric KL-Divergence of salient distributions that are derived from these matrix factors. We observe that the divergence values are higher for non-optimal number of topics – this is shown by a 'dip' at the right value for 't'.
494 citations
••
01 Apr 2014TL;DR: This work explores the two tasks of automatic Evaluation of single topics and automatic evaluation of whole topic models, and provides recommendations on the best strategy for performing the two task, in addition to providing an open-source toolkit for topic and topic model evaluation.
Abstract: Topic models based on latent Dirichlet allocation and related methods are used in a range of user-focused tasks including document navigation and trend analysis, but evaluation of the intrinsic quality of the topic model and topics remains an open research area. In this work, we explore the two tasks of automatic evaluation of single topics and automatic evaluation of whole topic models, and provide recommendations on the best strategy for performing the two tasks, in addition to providing an open-source toolkit for topic and topic model evaluation.
493 citations
••
TL;DR: The Author-Recipient-Topic model for social network analysis, which learns topic distributions based on the direction-sensitive messages sent between entities, is presented and results are given, providing evidence not only that clearly relevant topics are discovered, but that the ART model better predicts people's roles and gives lower perplexity on previously unseen messages.
Abstract: Previous work in social network analysis (SNA) has modeled the existence of links from one entity to another, but not the attributes such as language content or topics on those links. We present the Author-Recipient-Topic (ART) model for social network analysis, which learns topic distributions based on the direction-sensitive messages sent between entities. The model builds on Latent Dirichlet Allocation (LDA) and the Author-Topic (AT) model, adding the key attribute that distribution over topics is conditioned distinctly on both the sender and recipient--steering the discovery of topics according to the relationships between people. We give results on both the Enron email corpus and a researcher's email archive, providing evidence not only that clearly relevant topics are discovered, but that the ART model better predicts people's roles and gives lower perplexity on previously unseen messages. We also present the Role-Author-Recipient-Topic (RART) model, an extension to ART that explicitly represents people's roles.
484 citations
••
TL;DR: The key to the algorithm detailed in this article, which also keeps the random distribution functions, is the introduction of a latent variable which allows a finite number of objects to be sampled within each iteration of a Gibbs sampler.
Abstract: We provide a new approach to the sampling of the well known mixture of Dirichlet process model. Recent attention has focused on retention of the random distribution function in the model, but sampling algorithms have then suffered from the countably infinite representation these distributions have. The key to the algorithm detailed in this article, which also keeps the random distribution functions, is the introduction of a latent variable which allows a finite number, which is known, of objects to be sampled within each iteration of a Gibbs sampler.
482 citations