brainsteam.co.uk/brainsteam/content/annotations/2022/11/21/1669061389.md

---
date: '2022-11-21T20:09:49'
hypothesis-meta:
  created: '2022-11-21T20:09:49.369906+00:00'
  document:
    title:
    - IEEEtran-7.pdf
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  id: c71vQmnYEe2a7ffZp-667A
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  tags:
  - multi-task learning
  - topic modelling
  - NLProc
  target:
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      start: 7778
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    - 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)"
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      suffix: . At thesame time, mutual learni
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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: 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: annotation
url: /annotation/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