Question 1

How accurate is qTypes for real-world chatbot questions?

Accepted Answer

qTypes benchmarks at ~80% accuracy on coarse types using the TREC dataset, but performance may drop with informal or domain-specific language. Always test on your own data to ensure it meets accuracy needs.

Question 2

Can qTypes handle questions in Spanish or other languages?

Accepted Answer

No, qTypes is optimized for English only, as it's based on the English TREC dataset. Extending it to other languages would require manually adding new rules and patterns.

Question 3

qTypes vs machine learning: which is better for question classification?

Accepted Answer

qTypes is rule-based, so it's more transparent and easier to extend, but less accurate than trained ML models like those in spaCy. Choose qTypes for simplicity and interpretability, or ML for higher accuracy with more complexity.

Question 4

How to add custom rules to qTypes for specific question types?

Accepted Answer

Since qTypes is open-source and rule-based, you can extend it by modifying the source code. Look at the existing pattern definitions in the GitHub repository and add new rules following the same structure.

Question 5

Is qTypes good for educational apps or helpdesk systems?

Accepted Answer

Yes, qTypes is well-suited for categorizing user queries in educational or helpdesk contexts due to its simplicity and rule-based approach, but verify its accuracy with your specific question corpus.

Question 6

What datasets does qTypes support for training or evaluation?

Accepted Answer

qTypes is benchmarked against the TREC 10 labeled questions dataset, but it doesn't support training since it's rule-based. You can use it out-of-the-box or extend rules based on your data.

Qtypes

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