Answers.Body: <p>I realized the problem can be solved as such:</p>
  <p>Given a target text and a list of keywords, embed both using a sentence embedding model (such as MiniLM-L6-v2 from HuggingFace). The embeddings can then be compared using a similarity measure and classification can be done using a threshold value.</p>
  <p>This approach is topic agnostic, since the list of key terms can be changed for any topic that needs to be found.</p>
  
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Answers.CreationDate: 2022-09-08T14:14:19.093Z
Answers.LastActivityDate: 2022-09-08T14:14:19.093Z
Answers.UserDisplayName: Kroshtan
Body: <p>In the recent preprint paper <a href="https://arxiv.org/pdf/2112.07620.pdf" rel="nofollow noreferrer">Tree-based Focused Web Crawling with
  Reinforcement Learning</a> a new model is introduced to classify web pages called KwBiLSTM.</p>
  <p>The input to this model is a featurized webpage which, later in the forward pass, is concatenated with a featurized version of a list of keywords. Together, these features are passed to the classification layer, which determines whether the webpage and the keywords correspond to the same topic (label 1 or 0).</p>
  <p>From literature research I could not find any other work using this novel approach, namely that the topic being classified is supplied as an argument, rather than learned explicitly by providing a set of webpages labeled for a certain topic.</p>
  <p>My question is: Are there other works using a similar topic-agnostic binary topic classifier?</p>
  
Tags: <reference-request><papers><model-request>
Title: Is there any work done on topic agnostic binary topic classification?
UserDisplayName: Kroshtan
ThreadText: # Is there any work done on topic agnostic binary topic classification?
  
  (Asked by: username_0 on 2022-09-08)
  
  In the recent preprint paper [Tree-based Focused Web Crawling with
  Reinforcement Learning](https://arxiv.org/pdf/2112.07620.pdf) a new model is introduced to classify web pages called KwBiLSTM.
  
  The input to this model is a featurized webpage which, later in the forward pass, is concatenated with a featurized version of a list of keywords. Together, these features are passed to the classification layer, which determines whether the webpage and the keywords correspond to the same topic (label 1 or 0).
  
  From literature research I could not find any other work using this novel approach, namely that the topic being classified is supplied as an argument, rather than learned explicitly by providing a set of webpages labeled for a certain topic.
  
  My question is: Are there other works using a similar topic-agnostic binary topic classifier?
  
  **Tags:** reference-request, papers, model-request
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_0 on 2022-09-08. Score: 1 | Accepted answer
  
  I realized the problem can be solved as such:
  
  Given a target text and a list of keywords, embed both using a sentence embedding model (such as MiniLM-L6-v2 from HuggingFace). The embeddings can then be compared using a similarity measure and classification can be done using a threshold value.
  
  This approach is topic agnostic, since the list of key terms can be changed for any topic that needs to be found.
  
  
  ---
  
  