大厂100 NLP interview questions外企

CLASSIC NLP

TF-IDF & ML (8)

1. Write TF-IDF from scratch.

2. What is normalization in TF-IDF ?

3. Why do you need to know about TF-IDF in our time, and how can you use it in complex models?

4. Explain how Naive Bayes works. What can you use it for?

5. How can SVM be prone to overfitting?

6. Explain possible methods for text preprocessing ( lemmatization and stemming ). What algorithms do you know for this, and in what cases would you use them?

7. What metrics for text similarity do you know?

8. Explain the difference between cosine similarity and cosine distance. Which of these values can be negative? How would you use them?

METRICS (7)

9. Explain precision and recall in simple words and what you would look at in the absence of F1 score?

10. In what case would you observe changes in specificity ?

11. When would you look at macro, and when at micro metrics? Why does the weighted metric exist?

12. What is perplexity? What can we consider it with?

13. What is the BLEU metric?

14. Explain the difference between different types of ROUGE metrics?

15. What is the difference between BLUE and ROUGE?

WORD2VEC(9)

16. Explain how Word2Vec learns? What is the loss function? What is maximized?

17. What methods of obtaining embeddings do you know? When will each be better?

18. What is the difference between static and contextual embeddings?

19. What are the two main architectures you know, and which one learns faster?

20. What is the difference