Natural Language Processing Engineer Interview Preparation Guide
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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

78 Natural Language Processing Engineer Questions and Answers:

1 :: Tell me what is sequence learning?

Sequence learning is a method of teaching and learning in a logical manner.

2 :: Tell me what are the different methods for Sequential Supervised Learning?

The different methods to solve Sequential Supervised Learning problems are

☛ a) Sliding-window methods
☛ b) Recurrent sliding windows
☛ c) Hidden Markow models
☛ d) Maximum entropy Markow models
☛ e) Conditional random fields
☛ f) Graph transformer networks

3 :: Tell us what is bias-variance decomposition of classification error in ensemble method?

The expected error of a learning algorithm can be decomposed into bias and variance. A bias term measures how closely the average classifier produced by the learning algorithm matches the target function. The variance term measures how much the learning algorithm’s prediction fluctuates for different training sets.

4 :: Tell me what are the two classification methods that SVM ( Support Vector Machine) can handle?

☛ a) Combining binary classifiers
☛ b) Modifying binary to incorporate multiclass learning

5 :: Tell us what are the two methods used for the calibration in Supervised Learning?

The two methods used for predicting good probabilities in Supervised Learning are

☛ a) Platt Calibration
☛ b) Isotonic Regression

These methods are designed for binary classification, and it is not trivial.

6 :: Tell us in what areas Pattern Recognition is used?

Pattern Recognition can be used in

☛ a) Computer Vision
☛ b) Speech Recognition
☛ c) Data Mining
☛ d) Statistics
☛ e) Informal Retrieval
☛ f) Bio-Informatics

7 :: Explain me the function of ‘Unsupervised Learning’?

☛ a) Find clusters of the data
☛ b) Find low-dimensional representations of the data
☛ c) Find interesting directions in data
☛ d) Interesting coordinates and correlations
☛ e) Find novel observations/ database cleaning

8 :: Tell me what are the three stages to build the hypotheses or model in machine learning?

☛ a) Model building

☛ b) Model testing

☛ c) Applying the model

9 :: Tell us what is inductive machine learning?

The inductive machine learning involves the process of learning by examples, where a system, from a set of observed instances tries to induce a general rule.

10 :: Explain me the difference between Data Mining and Machine learning?

Machine learning relates with the study, design and development of the algorithms that give computers the capability to learn without being explicitly programmed. While, data mining can be defined as the process in which the unstructured data tries to extract knowledge or unknown interesting patterns. During this process machine, learning algorithms are used.

12 :: Tell me what are two techniques of Machine Learning?

The two techniques of Machine Learning are

☛ a) Genetic Programming
☛ b) Inductive Learning

13 :: Tell us what are the components of relational evaluation techniques?

The important components of relational evaluation techniques are

☛ a) Data Acquisition
☛ b) Ground Truth Acquisition
☛ c) Cross Validation Technique
☛ d) Query Type
☛ e) Scoring Metric
☛ f) Significance Test

14 :: Tell us the function of ‘Supervised Learning’?

☛ a) Classifications
☛ b) Speech recognition
☛ c) Regression
☛ d) Predict time series
☛ e) Annotate strings

15 :: Please explain how can you avoid overfitting?

By using a lot of data overfitting can be avoided, overfitting happens relatively as you have a small dataset, and you try to learn from it. But if you have a small database and you are forced to come with a model based on that. In such situation, you can use a technique known as cross validation. In this method the dataset splits into two section, testing and training datasets, the testing dataset will only test the model while, in training dataset, the datapoints will come up with the model.

In this technique, a model is usually given a dataset of a known data on which training (training data set) is run and a dataset of unknown data against which the model is tested. The idea of cross validation is to define a dataset to “test” the model in the training phase.

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

C) 12534

A right text classification model contains – cleaning of text to remove noise, annotation to create more features, converting text-based features into predictors, learning a model using gradient descent and finally tuning a model.

20 :: Do you know ‘Overfitting’ in Machine learning?

In machine learning, when a statistical model describes random error or noise instead of underlying relationship ‘overfitting’ occurs. When a model is excessively complex, overfitting is normally observed, because of having too many parameters with respect to the number of training data types. The model exhibits poor performance which has been overfit.