Answers.Body: <p>Instead of relying solely on machine learning models, you can develop a set of rule-based techniques to locate text blocks in the PDF based on specific patterns or keywords. This would involve leveraging your domain expert's knowledge to define rules that capture the essence of the regulations. While rule-based approaches may require manual effort to define and update the rules, they can be effective in cases where the rules are well-defined and straightforward.</p>
  <p>You can utilize text matching techniques, such as regular expressions or fuzzy string matching, to identify text blocks in the PDF that match specific regulation rules. By preprocessing your regulation rules and the PDF text, you can efficiently search for matches and extract relevant information.</p>
  <p>Consider employing information retrieval techniques to retrieve relevant text blocks from the PDF. You can use techniques like TF-IDF (term frequency-inverse document frequency) or BM25 (Okapi BM25) to rank the text blocks based on their similarity to the regulation rules.</p>
  <p>If you can generate a suitable dataset where domain experts label text blocks in the PDF as matching specific regulation rules, you can apply supervised learning techniques. One possible approach is to frame it as a multi-label text classification problem, where each regulation rule corresponds to a label that can be assigned to multiple text blocks. Models like BERT or other pre-trained transformer-based models can be fine-tuned for this task.</p>
  <p>It's worth mentioning that each approach has its own pros and cons, and the most suitable option depends on the complexity and structure of your regulations, the size of your dataset, the availability of domain expertise, and the accuracy requirements. Consider exploring a combination of these approaches or even experimenting with ensemble models to improve performance.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-12-11T09:36:22.627Z
Answers.LastActivityDate: 2023-12-11T09:36:22.627Z
Answers.UserDisplayName: han
Body: <p>I have a PDF written to disclose different corporate policies and governance implementations. It's a lot more but you get the gist.</p>
  <p>On the other hand, I have a set of rules and regulations that dictate what should be included in the PDF mentioned above. As expected, the rules and regulations are political and/or legal speak so will not directly translate into the text in the PDF.</p>
  <p>Assuming I have a domain expert who can match those two documents and generate the training data: What task and per-trained model would be good options in this scenario? Note: a block of text in the PDF above can match zero or more rules.</p>
  <p>I was leaning toward using text classification and having each rule trained as a label, but the dataset would be very difficult to generate.</p>
  <p>So I am open to other creative ideas. Stacked models, LLM's, Army of Monkeys...</p>
  
Tags: <models>
Title: What task/model to use to locate textblock in pdf that matches regulation rules
UserDisplayName: Kenth Fagerlund
ThreadText: # What task/model to use to locate textblock in pdf that matches regulation rules
  
  (Asked by: username_0 on 2023-12-07)
  
  I have a PDF written to disclose different corporate policies and governance implementations. It's a lot more but you get the gist.
  
  On the other hand, I have a set of rules and regulations that dictate what should be included in the PDF mentioned above. As expected, the rules and regulations are political and/or legal speak so will not directly translate into the text in the PDF.
  
  Assuming I have a domain expert who can match those two documents and generate the training data: What task and per-trained model would be good options in this scenario? Note: a block of text in the PDF above can match zero or more rules.
  
  I was leaning toward using text classification and having each rule trained as a label, but the dataset would be very difficult to generate.
  
  So I am open to other creative ideas. Stacked models, LLM's, Army of Monkeys...
  
  **Tags:** models
  
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  ## 1 answers
  
  ### Answer by username_1 on 2023-12-11. Score: 0
  
  Instead of relying solely on machine learning models, you can develop a set of rule-based techniques to locate text blocks in the PDF based on specific patterns or keywords. This would involve leveraging your domain expert's knowledge to define rules that capture the essence of the regulations. While rule-based approaches may require manual effort to define and update the rules, they can be effective in cases where the rules are well-defined and straightforward.
  
  You can utilize text matching techniques, such as regular expressions or fuzzy string matching, to identify text blocks in the PDF that match specific regulation rules. By preprocessing your regulation rules and the PDF text, you can efficiently search for matches and extract relevant information.
  
  Consider employing information retrieval techniques to retrieve relevant text blocks from the PDF. You can use techniques like TF-IDF (term frequency-inverse document frequency) or BM25 (Okapi BM25) to rank the text blocks based on their similarity to the regulation rules.
  
  If you can generate a suitable dataset where domain experts label text blocks in the PDF as matching specific regulation rules, you can apply supervised learning techniques. One possible approach is to frame it as a multi-label text classification problem, where each regulation rule corresponds to a label that can be assigned to multiple text blocks. Models like BERT or other pre-trained transformer-based models can be fine-tuned for this task.
  
  It's worth mentioning that each approach has its own pros and cons, and the most suitable option depends on the complexity and structure of your regulations, the size of your dataset, the availability of domain expertise, and the accuracy requirements. Consider exploring a combination of these approaches or even experimenting with ensemble models to improve performance.
  
  
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