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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
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Proceedings Article
23 Jul 2014
TL;DR: In this article, Bayesian optimization for constrained problems is studied in the general case that noise may be present in the constraint functions, and the objective and constraints may be evaluated independently.
Abstract: Recent work on Bayesian optimization has shown its effectiveness in global optimization of difficult black-box objective functions. Many real-world optimization problems of interest also have constraints which are unknown a priori. In this paper, we study Bayesian optimization for constrained problems in the general case that noise may be present in the constraint functions, and the objective and constraints may be evaluated independently. We provide motivating practical examples, and present a general framework to solve such problems. We demonstrate the effectiveness of our approach on optimizing the performance of online latent Dirichlet allocation subject to topic sparsity constraints, tuning a neural network given test-time memory constraints, and optimizing Hamiltonian Monte Carlo to achieve maximal effectiveness in a fixed time, subject to passing standard convergence diagnostics.

63 citations

Journal ArticleDOI
TL;DR: Topic modeling has the ability to segregate a large collection of articles into distinct themes, and it could be used as a tool to understand the literature, not only by recapturing known facts but also by discovering other relevant topics.
Abstract: Both adolescent substance use and adolescent depression are major public health problems, and have the tendency to co-occur. Thousands of articles on adolescent substance use or depression have been published. It is labor intensive and time consuming to extract huge amounts of information from the cumulated collections. Topic modeling offers a computational tool to find relevant topics by capturing meaningful structure among collections of documents. In this study, a total of 17,723 abstracts from PubMed published from 2000 to 2014 on adolescent substance use and depression were downloaded as objects, and Latent Dirichlet allocation (LDA) was applied to perform text mining on the dataset. Word clouds were used to visually display the content of topics and demonstrate the distribution of vocabularies over each topic. The LDA topics recaptured the search keywords in PubMed, and further discovered relevant issues, such as intervention program, association links between adolescent substance use and adolescent depression, such as sexual experience and violence, and risk factors of adolescent substance use, such as family factors and peer networks. Using trend analysis to explore the dynamics of proportion of topics, we found that brain research was assessed as a hot issue by the coefficient of the trend test. Topic modeling has the ability to segregate a large collection of articles into distinct themes, and it could be used as a tool to understand the literature, not only by recapturing known facts but also by discovering other relevant topics.

63 citations

Proceedings ArticleDOI
19 Apr 2009
TL;DR: A new hierarchical representation of words, sentences and documents in a corpus is presented, and the proposed sentence-based latent Dirichlet allocation (SLDA) outperforms other methods for document summarization in terms of precision, recall and F-measure.
Abstract: Automatic summarization is developed to extract the representative contents or sentences from a large corpus of documents. This paper presents a new hierarchical representation of words, sentences and documents in a corpus, and infers the Dirichlet distributions for latent topics and latent themes in word level and sentence level, respectively. The sentence-based latent Dirichlet allocation (SLDA) is accordingly established for document summarization. Different from the vector space summarization, SLDA is built to fit the fine structure of text documents, and is specifically designed for sentence selection. SLDA acts as a sentence mixture model with a mixture of Dirichlet themes, which are used to generate the latent topics in observed words. The theme model is inherent to distinguish sentences in a summarization system. In the experiments, the proposed SLDA outperforms other methods for document summarization in terms of precision, recall and F-measure.

63 citations

Book ChapterDOI
24 Mar 2013
TL;DR: The investigation on semantic similarity measures at word- and sentence-level based on two fully-automated approaches to deriving meaning from large corpora: Latent Dirichlet Allocation, a probabilistic approach, and Latent Semantic Analysis, an algebraic approach are presented.
Abstract: We present in this paper the results of our investigation on semantic similarity measures at word- and sentence-level based on two fully-automated approaches to deriving meaning from large corpora: Latent Dirichlet Allocation, a probabilistic approach, and Latent Semantic Analysis, an algebraic approach. The focus is on similarity measures based on Latent Dirichlet Allocation, due to its novelty aspects, while the Latent Semantic Analysis measures are used for comparison purposes. We explore two types of measures based on Latent Dirichlet Allocation: measures based on distances between probability distribution that can be applied directly to larger texts such as sentences and a word-to-word similarity measure that is then expanded to work at sentence-level. We present results using paraphrase identification data in the Microsoft Research Paraphrase corpus.

62 citations

Journal ArticleDOI
TL;DR: A text sentiment analysis method combining Latent Dirichlet Allocation text representation and convolutional neural network (CNN) that can effectively improve the accuracy of text sentiment classification.
Abstract: In order to improve the performance of internet public sentiment analysis, a text sentiment analysis method combining Latent Dirichlet Allocation (LDA) text representation and convolutional neural network (CNN) is proposed. First, the review texts are collected from the network for preprocessing. Then, using the LDA topic model to train the latent semantic space representation (topic distribution) of the short text, and the short text feature vector representation based on the topic distribution is constructed. Finally, the CNN with gated recurrent unit (GRU) is used as a classifier. According to the input feature matrix, the GRU-CNN strengthens the relationship between words and words, text and text, so as to achieve high accurate text classification. The simulation results show that this method can effectively improve the accuracy of text sentiment classification.

62 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023323
2022842
2021418
2020429
2019473
2018446