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+---
+date: '2022-11-21T20:07:22'
+hypothesis-meta:
+  created: '2022-11-21T20:07:22.691275+00:00'
+  document:
+    title:
+    - IEEEtran-7.pdf
+  flagged: false
+  group: __world__
+  hidden: false
+  id: HE4vnmnYEe2__KMWJ8Dgcg
+  links:
+    html: https://hypothes.is/a/HE4vnmnYEe2__KMWJ8Dgcg
+    incontext: https://hyp.is/HE4vnmnYEe2__KMWJ8Dgcg/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/HE4vnmnYEe2__KMWJ8Dgcg
+  permissions:
+    admin:
+    - acct:ravenscroftj@hypothes.is
+    delete:
+    - acct:ravenscroftj@hypothes.is
+    read:
+    - group:__world__
+    update:
+    - acct:ravenscroftj@hypothes.is
+  tags:
+  - NLProc
+  - multi-task learning
+  - topic modelling
+  target:
+  - selector:
+    - end: 5426
+      start: 5187
+      type: TextPositionSelector
+    - exact: . However, such a framework is not applicablehere since the learned latent
+        topic representations in topicmodels can not be shared directly with word
+        or sentencerepresentations learned in classifiers, due to their differentinherent
+        meanings
+      prefix: n task-relevant rep-resentations
+      suffix: .We instead propose a new MTL fr
+      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: Latent word vectors and topic models learn different and entirely unrelated
+    representations
+  updated: '2022-11-21T20:07:22.691275+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
+- multi-task learning
+- topic modelling
+- hypothesis
+type: reply
+url: /replies/2022/11/21/1669061242
+
+---
+
+
+
+ <blockquote>. However, such a framework is not applicablehere since the learned latent topic representations in topicmodels can not be shared directly with word or sentencerepresentations learned in classifiers, due to their differentinherent meanings</blockquote>Latent word vectors and topic models learn different and entirely unrelated representations