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---
date: '2022-11-21T20:07:22'
hypothesis-meta:
created: '2022-11-21T20:07:22.691275+00:00'
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- IEEEtran-7.pdf
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tags:
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- 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
suffix: .We instead propose a new MTL fr
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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:
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- 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