---
date: '2022-11-21T20:07:22'
hypothesis-meta:
  created: '2022-11-21T20:07:22.691275+00:00'
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    title:
    - IEEEtran-7.pdf
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  id: HE4vnmnYEe2__KMWJ8Dgcg
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  tags:
  - NLProc
  - multi-task learning
  - topic modelling
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    - 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
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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: 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: annotation
url: /annotation/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