Education and Science :: Natural Language Processing Engineer Job Interview Questions and Answers

Natural Language Processing Engineer related Frequently Asked Questions by expert members with job experience as Natural Language Processing Engineer. These questions and answers will help you strengthen your technical skills, prepare for the new job interview and quickly revise your concepts

1 Tell me what is sequence learning?
2 Tell me what are the different methods for Sequential Supervised Learning?
3 Tell us what is bias-variance decomposition of classification error in ensemble method?
4 Tell me what are the two classification methods that SVM ( Support Vector Machine) can handle?
5 Tell us what are the two methods used for the calibration in Supervised Learning?
6 Tell us in what areas Pattern Recognition is used?
7 Explain me the function of ‘Unsupervised Learning’?
8 Tell me what are the three stages to build the hypotheses or model in machine learning?
9 Tell us what is inductive machine learning?
10 Explain me the difference between Data Mining and Machine learning?
11 Collaborative Filtering and Content Based Models are the two popular recommendation engines, what role does NLP play in building such algorithms.    A) Feature Extraction from text  B) Measuring Feature Similarity  C) Engineering Features for vector space learning model  D) All of these
12 Tell me what are two techniques of Machine Learning?
13 Tell us what are the components of relational evaluation techniques?
14 Tell us the function of ‘Supervised Learning’?
15 Please explain how can you avoid overfitting?
16 Which of the following techniques can be used for the purpose of keyword normalization, the process of converting a keyword into its base form?    Lemmatization  Levenshtein  Stemming  Soundex    A) 1 and 2  B) 2 and 4  C) 1 and 3  D) 1, 2 and 3  E) 2, 3 and 4  F) 1, 2, 3 and 4
17 In Latent Dirichlet Allocation model for text classification purposes, what does alpha and beta hyperparameter represent-    A) Alpha: number of topics within documents, beta: number of terms within topics False  B) Alpha: density of terms generated within topics, beta: density of topics generated within terms False  C) Alpha: number of topics within documents, beta: number of terms within topics False  D) Alpha: density of topics generated within documents, beta: density of terms generated within topics True
18 What is the right order for a text classification model components    Text cleaning  Text annotation  Gradient descent  Model tuning  Text to predictors  A) 12345  B) 13425  C) 12534  D) 13452
19 Social Media platforms are the most intuitive form of text data. You are given a corpus of complete social media data of tweets. How can you create a model that suggests the hashtags?    A) Perform Topic Models to obtain most significant words of the corpus  B) Train a Bag of Ngrams model to capture top n-grams – words and their combinations  C) Train a word2vector model to learn repeating contexts in the sentences  D) All of these
20 Do you know ‘Overfitting’ in Machine learning?
21 Explain me what is the difference between heuristic for rule learning and heuristics for decision trees?
22 Tell me what is batch statistical learning?
23 Tell me what are the different categories you can categorized the sequence learning process?
24 N-grams are defined as the combination of N keywords together. How many bi-grams can be generated from given sentence:    “Analytics Vidhya is a great source to learn data science”    A) 7  B) 8  C) 9  D) 10  E) 11
25 You have created a document term matrix of the data, treating every tweet as one document. Which of the following is correct, in regards to document term matrix?    Removal of stopwords from the data will affect the dimensionality of data  Normalization of words in the data will reduce the dimensionality of data  Converting all the words in lowercase will not affect the dimensionality of the data  A) Only 1  B) Only 2  C) Only 3  D) 1 and 2  E) 2 and 3  F) 1, 2 and 3
26 True or False: Word2Vec model is a machine learning model used to create vector notations of text objects. Word2vec contains multiple deep neural networks    A) TRUE  B) FALSE
27 What are the possible features of a text corpus    Count of word in a document  Boolean feature – presence of word in a document  Vector notation of word  Part of Speech Tag  Basic Dependency Grammar  Entire document as a feature  A) 1  B) 12  C) 123  D) 1234  E) 12345  F) 123456
28 Retrieval based models and Generative models are the two popular techniques used for building chatbots. Which of the following is an example of retrieval model and generative model respectively.    A) Dictionary based learning and Word 2 vector model  B) Rule-based learning and Sequence to Sequence model  C) Word 2 vector and Sentence to Vector model  D) Recurrent neural network and convolutional neural network
29 Basic Natural Language Processing Engineer Job Interview Questions:
30 Common Natural Language Processing Engineer Job Interview Questions:
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32 Fresh Natural Language Processing Engineer Job Interview Questions:
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34 While working with context extraction from a text data, you encountered two different sentences: The tank is full of soldiers. The tank is full of nitrogen. Which of the following measures can be used to remove the problem of word sense disambiguation in the sentences?    A) Compare the dictionary definition of an ambiguous word with the terms contained in its neighborhood  B) Co-reference resolution in which one resolute the meaning of ambiguous word with the proper noun present in the previous sentence  C) Use dependency parsing of sentence to understand the meanings
35 Google Search’s feature – “Did you mean”, is a mixture of different techniques. Which of the following techniques are likely to be ingredients?    Collaborative Filtering model to detect similar user behaviors (queries)  Model that checks for Levenshtein distance among the dictionary terms  Translation of sentences into multiple languages  A) 1  B) 2  C) 1, 2  D) 1, 2, 3
36 Polysemy is defined as the coexistence of multiple meanings for a word or phrase in a text object. Which of the following models is likely the best choice to correct this problem?    A) Random Forest Classifier  B) Convolutional Neural Networks  C) Gradient Boosting  D) All of these
37 Solve the equation according to the sentence “I am planning to visit New Delhi to attend Analytics Vidhya Delhi Hackathon”.    A = (# of words with Noun as the part of speech tag)  B = (# of words with Verb as the part of speech tag)  C = (# of words with frequency count greater than one)    What are the correct values of A, B, and C?    A) 5, 5, 2  B) 5, 5, 0  C) 7, 5, 1  D) 7, 4, 2  E) 6, 4, 3
38 How many trigrams phrases can be generated from the following sentence, after performing following text cleaning steps:    Stopword Removal  Replacing punctuations by a single space  “#Analytics-vidhya is a great source to learn @data_science.”    A) 3  B) 4  C) 5  D) 6  E) 7
39 In a corpus of N documents, one document is randomly picked. The document contains a total of T terms and the term “data” appears K times.    What is the correct value for the product of TF (term frequency) and IDF (inverse-document-frequency), if the term “data” appears in approximately one-third of the total documents?    A) KT * Log(3)  B) K * Log(3) / T  C) T * Log(3) / K  D) Log(3) / KT
40 What is the major difference between CRF (Conditional Random Field) and HMM (Hidden Markov Model)?    A) CRF is Generative whereas HMM is Discriminative model  B) CRF is Discriminative whereas HMM is Generative model  C) Both CRF and HMM are Generative model  D) Both CRF and HMM are Discriminative model
41 Tell me what is ‘Training set’ and ‘Test set’?
42 Tell us why instance based learning algorithm sometimes referred as Lazy learning algorithm?
43 Tell me what are the areas in robotics and information processing where sequential prediction problem arises?
44 Which of the following regular expression can be used to identify date(s) present in the text object:    “The next meetup on data science will be held on 2017-09-21, previously it happened on 31/03, 2016”    A) d{4}-d{2}-d{2}  B) (19|20)d{2}-(0[1-9]|1[0-2])-[0-2][1-9] C) (19|20)d{2}-(0[1-9]|1[0-2])-([0-2][1-9]|3[0-1])  D) None of the above
45 Which of the following statement is(are) true for Word2Vec model?    A) The architecture of word2vec consists of only two layers – continuous bag of words and skip-gram model  B) Continuous bag of word is a shallow neural network model  C) Skip-gram is a deep neural network model  D) Both CBOW and Skip-gram are deep neural network models  E) All of the above
46 While working with text data obtained from news sentences, which are structured in nature, which of the grammar-based text parsing techniques can be used for noun phrase detection, verb phrase detection, subject detection and object detection.    A) Part of speech tagging  B) Dependency Parsing and Constituency Parsing  C) Skip Gram and N-Gram extraction  D) Continuous Bag of Words
47 Explain me what is the general principle of an ensemble method and what is bagging and boosting in ensemble method?
48 What percentage of the total statements are correct with regards to Topic Modeling?    It is a supervised learning technique  LDA (Linear Discriminant Analysis) can be used to perform topic modeling  Selection of number of topics in a model does not depend on the size of data  Number of topic terms are directly proportional to size of the data  A) 0  B) 25  C) 50  D) 75  E) 100
49 Explain what is algorithm independent machine learning?
50 Do you know the two components of Bayesian logic program?
51 Tell me what is an Incremental Learning algorithm in ensemble?
52 Do you know what is PAC Learning?
53 Which of the following features can be used for accuracy improvement of a classification model?    A) Frequency count of terms  B) Vector Notation of sentence  C) Part of Speech Tag  D) Dependency Grammar  E) All of these
54 While creating a machine learning model on text data, you created a document term matrix of the input data of 100K documents. Which of the following remedies can be used to reduce the dimensions of data –    Latent Dirichlet Allocation  Latent Semantic Indexing  Keyword Normalization  A) only 1  B) 2, 3  C) 1, 3  D) 1, 2, 3
55 Tell me what is the difference between artificial learning and machine learning?
56 Please explain what is PCA, KPCA and ICA used for?
57 Do you know what is Model Selection in Machine Learning?
58 Which of the following models can be used for the purpose of document similarity?    A) Training a word 2 vector model on the corpus that learns context present in the document  B) Training a bag of words model that learns occurrence of words in the document  C) Creating a document-term matrix and using cosine similarity for each document  D) All of the above
59 Which of the following technique is not a part of flexible text matching?    A) Soundex  B) Metaphone  C) Edit Distance  D) Keyword Hashing
60 Tell us what are the different Algorithm techniques in Machine Learning?
61 Tell me what are the two paradigms of ensemble methods?
62 Tell me what are the five popular algorithms of Machine Learning?
63 Tell me what is not Machine Learning?
64 Tell me what is Inductive Logic Programming in Machine Learning?
65 Tell me what are support vector machines?
66 Tell me what is classifier in machine learning?
67 Do you know why overfitting happens?
68 Tell me what are Bayesian Networks (BN)?
69 Tell me when to use ensemble learning?
70 Tell me what are the advantages of Naive Bayes?
71 Do you know what is the standard approach to supervised learning?
72 Tell us why ensemble learning is used?
73 Tell me various approaches for machine learning?
74 Tell us what is dimension reduction in Machine Learning?
75 Explain what is ensemble learning?
76 Tell me which method is frequently used to prevent overfitting?
77 Explain me what is Genetic Programming?
78 Please explain what is Perceptron in Machine Learning?

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