TOP 100 Natural Language Processing Interview Questions.

 Natural Language Processing  (NLP) Interview Questions :

NLP interview questions.

Q1. Which one of the following are keyword normalization technique in NLP?

     a. Stemming

     b. Named entity recognition

     c. Lemmatization.

     d. Part of Speech

Ans: a) and d) both

     Part of speech (POS) and Named Entity Recognition (NER) are not keyword normalization techniques. Named Entity help you extract Organization, Time, Date, city etc.

Q2. Which of the below are NLP use cases?

     a. Detecting Objects from an image

     b. Facial Recognition

     c. Speech Biometric

    d. Text Summarization

Ans: (d)

     a) and b) are computer vision use cases and c) is Speech use case. only d) Text Summarization is an NLP use case.

Q3. In NLP , The process of removing words like "and",  "is",  "a", "the", from a  sentence is called as

    a. Stemming

    b. Lemmatization

    c. Stop word

    d. All of the above

Ans: (c)

    In Lemmatization all the stop words such as a, an, the etc. are  removed. One can  also define custom stop words for removal.

Q4. In NLP, The process of converting a sentence or paragraph into tokens is reffered to as Stemming

    a. True

    b. False

Ans: (b) The statement describes the process of tokenization and not stemming.  hence it is False.

Q5. In NLP, Tokens are converted into numbers before giving to  any neural network

    a. True

    b. False

Ans: (a) in NLP, all words are converted into a number before  feeding to a neural network. hence it is True.

Q6. Identify the odd one out

    a. nltk

    b. scikit learn

    c. BERT

    d. SpaCy

Ans: (C) All the ones mentioned are NLP libraries except BERT,  which is a word  embedding.

Q7. In NLP, The process of identifying poeple , an organization from a given sentence, paragraph is called as

    a. Stemming

    b. Lemmatization

    c. Stop word removal

    d. Named entity recognition

Ans: (d)

Q8. Which one of the following is not a pre-processing  technique in NLP

    a. Stemming and Lemmatization

    b. converting to lowercase

    c. removing punctuations

    d. removal of stop words

    e. sentiment analysis

Ans: (e) Sentiment Analysis isnot a pre-processing technique. It is done after pre-processing and is an NLP use case. All other listed ones are uses as part of  statement pre-processing.

Q9.  In text mining, converting text into tokens and then converting them into  an  integer or floating-point vector can be done using

    a. CountVectorizer

    b. TF-IDF

    c. Bag of Words

    d. NERs

Ans: (a) CountVectorizer helps to do the above, while others are not applicable

Q10. In NLP, Words represented as vectors are called as Neural Words Embeddings 

    a. True

    b. False

Ans: (a) Word2Vec, GloVe based models build word embedding voctors tha are multidimensional.

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