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+---
+date: '2022-11-21T20:09:49'
+hypothesis-meta:
+  created: '2022-11-21T20:09:49.369906+00:00'
+  document:
+    title:
+    - IEEEtran-7.pdf
+  flagged: false
+  group: __world__
+  hidden: false
+  id: c71vQmnYEe2a7ffZp-667A
+  links:
+    html: https://hypothes.is/a/c71vQmnYEe2a7ffZp-667A
+    incontext: https://hyp.is/c71vQmnYEe2a7ffZp-667A/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/c71vQmnYEe2a7ffZp-667A
+  permissions:
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+    delete:
+    - acct:ravenscroftj@hypothes.is
+    read:
+    - group:__world__
+    update:
+    - acct:ravenscroftj@hypothes.is
+  tags:
+  - multi-task learning
+  - topic modelling
+  - NLProc
+  target:
+  - selector:
+    - end: 8039
+      start: 7778
+      type: TextPositionSelector
+    - exact: "e argue that mutual learningwould benefit sentiment classification since\
+        \ it enriches theinformation required for the training of the sentiment clas-sifier\
+        \ (e.g., when the word \u201Cincredible\u201D is used to describe\u201Cacting\u201D\
+        \ or \u201Cmovie\u201D, the polarity should be positive)"
+      prefix: "thewords \u201Cacting\u201D and \u201Cmovie\u201D. W"
+      suffix: . At thesame time, mutual learni
+      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: By training a topic model that has "similar" weights to the word vector model
+    the sentiment task can also be improved (as per the example "incredible" should
+    be positive when used to describe "acting" or "movie" in this context
+  updated: '2022-11-21T20:09:49.369906+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:
+- multi-task learning
+- topic modelling
+- NLProc
+- hypothesis
+type: reply
+url: /replies/2022/11/21/1669061389
+
+---
+
+
+
+ <blockquote>e argue that mutual learningwould benefit sentiment classification since it enriches theinformation required for the training of the sentiment clas-sifier (e.g., when the word “incredible” is used to describe“acting” or “movie”, the polarity should be positive)</blockquote>By training a topic model that has "similar" weights to the word vector model the sentiment task can also be improved (as per the example "incredible" should be positive when used to describe "acting" or "movie" in this context