<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>

CC BY-SA 4.0
2022-11-23T08:55:25.833Z
2022-11-23T08:55:25.833Z
Oliver Mason
<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It is not quite clear to me whether the statistical approach superseded the rule-based system in the 90s.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>

<machine-learning><natural-language-processing><statistical-ai><rule-based-systems>
Did old-fashioned, rule-based systems die out when statistical learning broke through into NLP?
Filippo Colombi
# 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It is not quite clear to me whether the statistical approach superseded the rule-based system in the 90s.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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