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---
date: '2022-11-21T20:13:05'
hypothesis-meta:
created: '2022-11-21T20:13:05.556810+00:00'
document:
title:
- IEEEtran-7.pdf
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id: 6KsbkmnYEe2Y3g9fobLUFA
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tags:
- NLProc
- topic modelling
- neural networks
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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'
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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,”