tailieunhanh - Some methods for posterior inference in topic models

The problem of posterior inference for individual documents is particularly important in topic models. However, it is often intractable in practice. Many existing methods for posterior inference such as variational Bayes, collapsed variational Bayes and collapsed Gibbs sampling do not have any guarantee on either quality or rate of convergence. | Research and Development on Information and Communication Technology Some Methods for Posterior Inference in Topic Models Xuan Bui1 2 Tu Vu1 Khoat Than1 1 Hanoi University of Science and Technology Hanoi Vietnam 2 Thai Nguyen University of Information and Communication Technology Vietnam Correspondence Xuan Bui thanhxuan1581@ Communication received 27 February 2018 revised 10 July 2018 accepted 8 August 2018 Online early access 8 November 2018 Digital Object Identifier The Area Editor coordinating the review of this article and deciding to accept it was Dr. Trinh Quoc Anh Abstract The problem of posterior inference for individual documents is particularly important in topic models. However it is often intractable in practice. Many existing methods for posterior inference such as variational Bayes collapsed variational Bayes and collapsed Gibbs sampling do not have any guarantee on either quality or rate of convergence. The online maximum a posteriori estimation OPE algorithm has more attractive properties than other inference approaches. In this paper we introduced four algorithms to improve OPE namely OpE1 OPE2 OPE3 and OPE4 by combining two stochastic bounds. Our new algorithms not only preserve the key advantages of OPE but also can sometimes perform significantly better than OPE. These algorithms were employed to develop new effective methods for learning topic models from massive streaming text collections. Empirical results show that our approaches were often more efficient than the state-of-the-art methods. Keywords Topic models posterior inference online maximum a posteriori estimation OPE large-scale learning. I. Introduction Topic modeling provides a framework to model highdimensional sparse data. It can also be seen as an unsupervised learning approach in machine learning. One of the most famous topic models latent Dirichlet allocation LDA 1 has been successfully applied in a wide range of areas including text .

• Cơ sở dữ liệu
• Topic models
• Posterior inference
• Online maximum a posteriori estimation
• Large scale learning
• Streaming text collections
• BMC Bioinformatics
• Dynamic topic model
• Times series gene expression
• Topic modeling
• Latent dirichlet model
• Dynamic Translation Model Adaptation
• Vladimir Eidelman
• báo cáo khoa học
• báo cáo ngôn ngữ
• ngôn ngữ tự nhiên
• Authorship Attribution
• Author aware Topic Models
• Yanir Seroussi
• Word Sense Disambiguation
• Token based Idiom Detection
• PCFGs Topic Models
• Adaptor Grammars and Learning Topical Collocations
• the Structure of Proper Names
• Automatic Labelling
• Jey Han Lau
• Identifying Word Translations
• Comparable Corpora
• Latent Topic Models
• Employing Topic Models
• Pattern based Semantic Class Discovery
• Mingjie Zhu
• xử lý ngôn ngữ tự nhiên
• Multi Document Summarization
• Sentence based Topic Models
• Dingding Wang
• Unsupervised Topic Identification
• Integrating Linguistic
• Visual Information Based on Hidden Markov Models
• mô hình ngôn ngữ
• Bilingual Topic AdMixture Models
• Word Alignment
• Bing Zhao
• Incorporating topic information
• sentiment analysis models
• Tony Mullen
• Statistical Models
• Topic Segmentation
• Jeffrey C
• Reynar
• Incorporating Lexical Priors
• Jagadeesh Jagarlamudi
• scientific reports
• model language
• process natural language
• Financial modeling
• Lecture Financial modeling
• Asset expected return
• Accrued interest
• Yield curve models
• Forward rates
• Financial statement simulation
• Cash flow
• Cash flow models
• Financial simulations
• Retirement ratios
• Changing asset allocation
• Sequence motifs
• Transcription factor binding sites
• Molecular biology
• DNA sequences
• User defined function
• Calculating cost of capital
• Portfolio models
• Estimating asset risk
• Asset risk
• Portfolio risk
• Computing portfolio risk