---
date: '2022-11-21T20:13:05'
hypothesis-meta:
  created: '2022-11-21T20:13:05.556810+00:00'
  document:
    title:
    - IEEEtran-7.pdf
  flagged: false
  group: __world__
  hidden: false
  id: 6KsbkmnYEe2Y3g9fobLUFA
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  tags:
  - NLProc
  - topic modelling
  - neural networks
  target:
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    - exact: n recent years, the neural network based topic modelshave been proposed
        for many NLP tasks, such as infor-mation retrieval [11], aspect extraction
        [12] and sentimentclassification [13]. The basic idea is to construct a neuralnetwork
        which aims to approximate the topic-word distri-bution in probabilistic topic
        models. Additional constraints,such as incorporating prior distribution [14],
        enforcing di-versity among topics [15] or encouraging topic sparsity [16],have
        been explored for neural topic model learning andproved effective.
      prefix: ' word embeddings[8], [9], [10].I'
      suffix: ' However, most of these algorith'
      type: TextQuoteSelector
    source: https://www.researchgate.net/profile/Lin-Gui-5/publication/342058196_Multi-Task_Learning_with_Mutual_Learning_for_Joint_Sentiment_Classification_and_Topic_Detection/links/5f96fd48458515b7cf9f3abd/Multi-Task-Learning-with-Mutual-Learning-for-Joint-Sentiment-Classification-and-Topic-Detection.pdf
  text: "Neural topic models are often trained to mimic the behaviours of probabilistic\
    \ topic models - I should come back and look at some of the works:\n\n* R. Das,\
    \ M. Zaheer, and C. Dyer, \u201CGaussian LDA for topic models with word embeddings,\u201D\
    \ \n* P. Xie, J. Zhu, and E. P. Xing, \u201CDiversity-promoting bayesian learning\
    \ of latent variable models,\u201D\n * M. Peng, Q. Xie, H. Wang, Y. Zhang, X.\
    \ Zhang, J. Huang, and G. Tian, \u201CNeural sparse topical coding,\u201D"
  updated: '2022-11-21T20:13:05.556810+00:00'
  uri: https://www.researchgate.net/profile/Lin-Gui-5/publication/342058196_Multi-Task_Learning_with_Mutual_Learning_for_Joint_Sentiment_Classification_and_Topic_Detection/links/5f96fd48458515b7cf9f3abd/Multi-Task-Learning-with-Mutual-Learning-for-Joint-Sentiment-Classification-and-Topic-Detection.pdf
  user: acct:ravenscroftj@hypothes.is
  user_info:
    display_name: James Ravenscroft
in-reply-to: https://www.researchgate.net/profile/Lin-Gui-5/publication/342058196_Multi-Task_Learning_with_Mutual_Learning_for_Joint_Sentiment_Classification_and_Topic_Detection/links/5f96fd48458515b7cf9f3abd/Multi-Task-Learning-with-Mutual-Learning-for-Joint-Sentiment-Classification-and-Topic-Detection.pdf
tags:
- NLProc
- topic modelling
- neural networks
- hypothesis
type: reply
url: /replies/2022/11/21/1669061585

---



 <blockquote>n recent years, the neural network based topic modelshave been proposed for many NLP tasks, such as infor-mation retrieval [11], aspect extraction [12] and sentimentclassification [13]. The basic idea is to construct a neuralnetwork which aims to approximate the topic-word distri-bution in probabilistic topic models. Additional constraints,such as incorporating prior distribution [14], enforcing di-versity among topics [15] or encouraging topic sparsity [16],have been explored for neural topic model learning andproved effective.</blockquote>Neural topic models are often trained to mimic the behaviours of probabilistic topic models - I should come back and look at some of the works:

* R. Das, M. Zaheer, and C. Dyer, “Gaussian LDA for topic models with word embeddings,” 
* P. Xie, J. Zhu, and E. P. Xing, “Diversity-promoting bayesian learning of latent variable models,”
 * M. Peng, Q. Xie, H. Wang, Y. Zhang, X. Zhang, J. Huang, and G. Tian, “Neural sparse topical coding,”