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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
+  flagged: false
+  group: __world__
+  hidden: false
+  id: 6KsbkmnYEe2Y3g9fobLUFA
+  links:
+    html: https://hypothes.is/a/6KsbkmnYEe2Y3g9fobLUFA
+    incontext: https://hyp.is/6KsbkmnYEe2Y3g9fobLUFA/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
+    json: https://hypothes.is/api/annotations/6KsbkmnYEe2Y3g9fobLUFA
+  permissions:
+    admin:
+    - acct:ravenscroftj@hypothes.is
+    delete:
+    - acct:ravenscroftj@hypothes.is
+    read:
+    - group:__world__
+    update:
+    - acct:ravenscroftj@hypothes.is
+  tags:
+  - NLProc
+  - topic modelling
+  - neural networks
+  target:
+  - selector:
+    - end: 11125
+      start: 10591
+      type: TextPositionSelector
+    - 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,”