Natural Language Processing Engineer Interview Preparation Guide

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

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

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.

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

Pattern Recognition can be used in

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

☛ 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

☛ a) Model building

☛ b) Model testing

☛ c) Applying the model

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.

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.

D) All of these

NLP can be used anywhere where text data is involved – feature extraction, measuring feature similarity, create vector features of the text.

The two techniques of Machine Learning are

☛ a) Genetic Programming
☛ b) Inductive Learning

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

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

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.

C) 1 and 3
Lemmatization and stemming are the techniques of keyword normalization, while Levenshtein and Soundex are techniques of string matching.

D) Alpha: density of topics generated within documents, beta: density of terms generated within topics True

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.

D) All of these

All of the techniques can be used to extract most significant terms of a corpus.

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.