Here are the top NLP interview questions to prepare for your next role.
1️⃣ Provide an example showing the difference between stemming and lemmatization for the word "running".
- A) "running" -> "run" using stemming; "running" -> "run" using lemmatization
- B) "running" -> "run" using stemming; "running" -> "run" using lemmatization
- C) "running" -> "runn" using stemming; "running" -> "run" using lemmatization
- D) "running" -> "runn" using stemming; "running" -> "runn" using lemmatization
2️⃣ Can you describe what the Bag-of-Words (BoW) model is?
- A) It is a model where text is represented as a set of unique words disregarding the order and frequency of words.
- B) It is a model where text is represented by disregarding grammar and word sequence, focusing only on the occurrence of words.
- C) It is a model that considers the syntactic structure and semantics of the text to generate vectors reflecting contextual meaning.
- D) It is a model that uses deep learning techniques to generate word embeddings capturing semantic relationships.
3️⃣ Can you explain what N-grams are in Natural Language Processing?
- A) N-grams are contiguous sequences of N items (words, characters, or tokens) from text used to capture context and word relationships in NLP tasks.
- B) N-grams are neural network layers where N represents the number of neurons in each hidden layer of the model.
- C) N-grams are the number of training epochs required for an NLP model to converge, where N is calculated based on dataset size.
- D) N-grams are normalization coefficients applied to word embeddings to ensure all vectors have magnitude N for consistent processing.
4️⃣ Can you explain the concept of a word embedding?
- A) A way to represent semantic meaning of words in a continuous vector space
- B) A technique to count word frequency in a document
- C) A method of matching patterns in text using regular expressions
- D) A process to encode words as indices in a vocabulary list
5️⃣ How does a TF-IDF vector differ from a Word2Vec vector?
- A) TF-IDF vectors are based on the frequency of words, while Word2Vec vectors are learned through neural networks.
- B) TF-IDF vectors capture semantic relationships between words, whereas Word2Vec vectors capture syntactic relationships.
- C) TF-IDF vectors do not consider word context beyond individual documents, whereas Word2Vec vectors use context from the entire corpus of text.
- D) TF-IDF vectors are generated using deep learning models, while Word2Vec vectors are generated using statistical methods.