NLP Interview Questions For Freshers

What is the purpose of syntactic parsing?

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What is the Seq2Seq model?

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Explain the concept of attention mechanism in NLP.

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What is the BLEU score?

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What is Co-reference resolution?

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What is the purpose of WordNet?

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What is the TF-IDF algorithm?

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What is the Bag-of-Words model?

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What is the difference between supervised and unsupervised learning in NLP?

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Explain the concept of word embeddings.

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What is topic modeling?

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What is sentiment analysis?

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What are the advantages and disadvantages of rule-based systems in NLP?

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What is language modeling?

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What is named entity recognition?

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What is POS tagging?

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What are stop words in NLP?

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What is lemmatization?

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What is stemming in NLP?

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Explain the concept of tokenization.

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What are the challenges faced in NLP?

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Give examples of NLP applications in everyday life.

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What are the main goals of NLP?

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What is NLP?

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What is the difference between bagging and boosting in machine learning?

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What are some popular libraries or tools used in NLP?

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Explain the concept of perplexity in language modeling.

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What are the main components of a neural network used in NLP?

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What is the purpose of attention heads in transformers?

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Explain the concept of zero-shot learning in NLP.

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What is the Transformer architecture?

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What is the difference between rule-based and statistical NLP?

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Explain the concept of transfer learning in NLP.

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NLP Intermediate Interview Questions

What is the difference between shallow and deep parsing?

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Explain the concept of recursive neural networks.

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What is the purpose of automatic speech recognition?

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What is the difference between word sense disambiguation and word sense induction?

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What are some deep learning frameworks used in NLP?

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Explain the concept of perplexity in machine translation.

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What is the purpose of named entity linking?

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Explain the concept of automatic summarization.

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What is the purpose of dependency parsing in NLP?

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What are the challenges faced in machine translation?

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What is the purpose of co-occurrence matrix in word embeddings?

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Explain the concept of named entity disambiguation.

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What is the purpose of attention mechanism in machine translation?

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What are the advantages and disadvantages of using RNNs?

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What is a recurrent neural network (RNN) in NLP?

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What is the purpose of word alignment in machine translation?

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Explain the concept of word2vec skip-gram model.

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What is the purpose of language modeling evaluation?

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What is the purpose of word vectorization in NLP?

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What are some evaluation metrics used in NLP?

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Explain the concept of word sense disambiguation.

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What is the purpose of attention in encoder-decoder models?

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How can you evaluate the performance of a language model?

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What is the difference between syntax and semantics?

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What is the purpose of language generation in NLP?

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What is a context-free grammar?

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What are the differences between Word2Vec and GloVe?

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Explain the concept of context window in word2vec.

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What are the different types of word embeddings?

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How can you preprocess text data in NLP?

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NLP Interview Questions For Experienced

Explain the concept of transformer-based language models.

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What are the applications of deep learning in NLP?

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What is the purpose of deep reinforcement learning in NLP?

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Explain the concept of beam search in machine translation.

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How can you handle out-of-vocabulary words in NLP?

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What is the difference between attention and self-attention?

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What is the purpose of reinforcement learning in machine translation?

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What is the difference between traditional machine translation and neural machine translation?

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Explain the concept of deep reinforcement learning for dialogue systems.

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What is the purpose of unsupervised learning in machine translation?

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What are the challenges faced in building dialogue systems?

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What is the purpose of deep contextualized word representations?

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Explain the concept of graph neural networks in NLP.

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How can you handle lexical ambiguity in NLP?

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What is the purpose of non-autoregressive machine translation?

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What is the difference between joint and pipeline models in NLP?

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Explain the concept of adversarial training in NLP.

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What are the challenges faced in building multilingual models?

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Explain the concept of denoising autoencoders in NLP.

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What is the purpose of deep generative models in NLP?

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What is the difference between structured prediction and sequence labeling?

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Explain the concept of word2vec continuous bag-of-words model.

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What is the purpose of unsupervised pretraining in NLP?

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What are the limitations of using transformer models?

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Explain the concept of hierarchical attention in NLP.

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How can you handle long sentences in transformers?

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What is the purpose of pointer networks in NLP?

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