scholarly journals Correction: A correlated topic model of Science

2007 ◽  
Vol 1 (2) ◽  
pp. 634-634 ◽  
Author(s):  
David M. Blei ◽  
John D. Lafferty
Keyword(s):  
Topic Model ◽  
Correlated Topic Model

scholarly journals Probit Normal Correlated Topic Model

2014 ◽  
Vol 04 (11) ◽  
pp. 879-888
Author(s):  
Xingchen Yu ◽  
Ernest Fokoué
Keyword(s):  
Topic Model ◽  
Correlated Topic Model

scholarly journals TopicEq: A Joint Topic and Mathematical Equation Model for Scientific Texts

2019 ◽  
Vol 33 ◽  
pp. 7394-7401 ◽  
Author(s):  
Michihiro Yasunaga ◽  
John D. Lafferty
Keyword(s):  
Topic Model ◽  
Equation Model ◽  
Scientific Texts ◽  
Mathematical Symbols ◽  
Variational Autoencoder ◽  
Mathematical Equations ◽  
And Mathematics ◽  
Novel Applications ◽  
Correlated Topic Model ◽  
The Relationship

Scientific documents rely on both mathematics and text to communicate ideas. Inspired by the topical correspondence between mathematical equations and word contexts observed in scientific texts, we propose a novel topic model that jointly generates mathematical equations and their surrounding text (TopicEq). Using an extension of the correlated topic model, the context is generated from a mixture of latent topics, and the equation is generated by an RNN that depends on the latent topic activations. To experiment with this model, we create a corpus of 400K equation-context pairs extracted from a range of scientific articles from arXiv, and fit the model using a variational autoencoder approach. Experimental results show that this joint model significantly outperforms existing topic models and equation models for scientific texts. Moreover, we qualitatively show that the model effectively captures the relationship between topics and mathematics, enabling novel applications such as topic-aware equation generation, equation topic inference, and topic-aware alignment of mathematical symbols and words.

scholarly journals The Complex Action Recognition via the Correlated Topic Model

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Hong-bin Tu ◽  
Li-min Xia ◽  
Zheng-wu Wang
Keyword(s):  
Optical Flow ◽  
Action Recognition ◽  
Topic Model ◽  
Body Part ◽  
Research Area ◽  
Body Parts ◽  
Complex Action ◽  
Motion Feature ◽  
Correlated Topic Model ◽  
Human Complex

Human complex action recognition is an important research area of the action recognition. Among various obstacles to human complex action recognition, one of the most challenging is to deal with self-occlusion, where one body part occludes another one. This paper presents a new method of human complex action recognition, which is based on optical flow and correlated topic model (CTM). Firstly, the Markov random field was used to represent the occlusion relationship between human body parts in terms of an occlusion state variable. Secondly, the structure from motion (SFM) is used for reconstructing the missing data of point trajectories. Then, we can extract the key frame based on motion feature from optical flow and the ratios of the width and height are extracted by the human silhouette. Finally, we use the topic model of correlated topic model (CTM) to classify action. Experiments were performed on the KTH, Weizmann, and UIUC action dataset to test and evaluate the proposed method. The compared experiment results showed that the proposed method was more effective than compared methods.

scholarly journals A Correlated Topic Model Using Word Embeddings

2017 ◽  
Author(s):  
Guangxu Xun ◽  
Yaliang Li ◽  
Wayne Xin Zhao ◽  
Jing Gao ◽  
Aidong Zhang
Keyword(s):  
Data Augmentation ◽  
Topic Model ◽  
Semantic Relatedness ◽  
Word Embedding ◽  
Word Embeddings ◽  
Word Level ◽  
Logistic Normal Distribution ◽  
Proposed Model ◽  
Correlation Information ◽  
Correlated Topic Model

Conventional correlated topic models are able to capture correlation structure among latent topics by replacing the Dirichlet prior with the logistic normal distribution. Word embeddings have been proven to be able to capture semantic regularities in language. Therefore, the semantic relatedness and correlations between words can be directly calculated in the word embedding space, for example, via cosine values. In this paper, we propose a novel correlated topic model using word embeddings. The proposed model enables us to exploit the additional word-level correlation information in word embeddings and directly model topic correlation in the continuous word embedding space. In the model, words in documents are replaced with meaningful word embeddings, topics are modeled as multivariate Gaussian distributions over the word embeddings and topic correlations are learned among the continuous Gaussian topics. A Gibbs sampling solution with data augmentation is given to perform inference. We evaluate our model on the 20 Newsgroups dataset and the Reuters-21578 dataset qualitatively and quantitatively. The experimental results show the effectiveness of our proposed model.

scholarly journals What Is Important When We Evaluate Movies? Insights from Computational Analysis of Online Reviews

2020 ◽  
Vol 8 (3) ◽  
pp. 153-163 ◽  
Author(s):  
Frank M. Schneider ◽  
Emese Domahidi ◽  
Felix Dietrich
Keyword(s):  
Large Scale ◽  
Topic Model ◽  
Evaluation Criteria ◽  
Online Reviews ◽  
Self Report ◽  
Confirmatory Factor Analyses ◽  
Report Data ◽  
Self Report Data ◽  
Entertainment Products ◽  
Correlated Topic Model

The question of what is important when we evaluate movies is crucial for understanding how lay audiences experience and evaluate entertainment products such as films. In line with this, subjective movie evaluation criteria (SMEC) have been conceptualized as mental representations of important attitudes toward specific film features. Based on exploratory and confirmatory factor analyses of self-report data from online surveys, previous research has found and validated eight dimensions. Given the large-scale evaluative information that is available in online users’ comments in movie databases, it seems likely that what online users write about movies may enrich our knowledge about SMEC. As a first fully exploratory attempt, drawing on an open-source dataset including movie reviews from IMDb, we estimated a correlated topic model to explore the underlying topics of those reviews. In 35,136 online movie reviews, the most prevalent topics tapped into three major categories—Hedonism, Actors’ Performance, and Narrative—and indicated what reviewers mostly wrote about. Although a qualitative analysis of the reviews revealed that users mention certain SMEC, results of the topic model covered only two SMEC: Story Innovation and Light-heartedness. Implications for SMEC and entertainment research are discussed.

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