Answers.Body: <p>No. In commercial applications, rule-based systems are still widely used, because they are easier to explain and debug: if there is an error, you add or change a rule. In a statistical system you need to adjust the training data, re-train your system, and hope that it solved the problem.</p>
  <p>What is sometimes used is a hybrid approach, where eg a stochastic part-of-speech tagger is used in conjunction with post-processing rules.</p>
  <p>In some commercial areas (eg finance) there are regulatory constraints that mean you cannot rely purely on statistical systems.</p>
  <p>[Note: obviously I cannot cite any sources, as this is not the kind of thing written about in academic papers. But I have in the past ten years worked at a number of businesses working on NLP, and they predominantly use rule-based systems, with a small proportion of stochastic components to support the rule-based one (intent-recognition, sentiment analysis, and pos-tagging).]</p>
  
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Answers.CreationDate: 2022-11-23T08:55:25.833Z
Answers.LastActivityDate: 2022-11-23T08:55:25.833Z
Answers.UserDisplayName: Oliver Mason
Body: <p>It is not quite clear to me whether the statistical approach superseded the rule-based system in the 90s.</p>
  <p><a href="https://link.springer.com/content/pdf/10.1023/A:1006517723917.pdf" rel="nofollow noreferrer">McMahon and Smith (1998)</a> report that many other researchers used &quot;hybrids of statistical and formal approaches&quot;. What do they mean? Maybe it was still unclear which one was best suited for?</p>
  
Tags: <machine-learning><natural-language-processing><statistical-ai><rule-based-systems>
Title: Did old-fashioned, rule-based systems die out when statistical learning broke through into NLP?
UserDisplayName: Filippo Colombi
ThreadText: # Did old-fashioned, rule-based systems die out when statistical learning broke through into NLP?
  
  (Asked by: username_0 on 2022-11-22)
  
  It is not quite clear to me whether the statistical approach superseded the rule-based system in the 90s.
  
  [McMahon and Smith (1998)](https://link.springer.com/content/pdf/10.1023/A:1006517723917.pdf) report that many other researchers used "hybrids of statistical and formal approaches". What do they mean? Maybe it was still unclear which one was best suited for?
  
  **Tags:** machine-learning, natural-language-processing, statistical-ai, rule-based-systems
  
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  ## 1 answers
  
  ### Answer by username_1 on 2022-11-23. Score: 1 | Accepted answer
  
  No. In commercial applications, rule-based systems are still widely used, because they are easier to explain and debug: if there is an error, you add or change a rule. In a statistical system you need to adjust the training data, re-train your system, and hope that it solved the problem.
  
  What is sometimes used is a hybrid approach, where eg a stochastic part-of-speech tagger is used in conjunction with post-processing rules.
  
  In some commercial areas (eg finance) there are regulatory constraints that mean you cannot rely purely on statistical systems.
  
  [Note: obviously I cannot cite any sources, as this is not the kind of thing written about in academic papers. But I have in the past ten years worked at a number of businesses working on NLP, and they predominantly use rule-based systems, with a small proportion of stochastic components to support the rule-based one (intent-recognition, sentiment analysis, and pos-tagging).]
  
  
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