NLP Interview Questions and Answers for 2023
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Natural Language Processing (NLP) allows machines to analyze and understand natural language. It plays a vital role in today’s era because of the sheer volume of text data that users generate around the world on digital channels such as social media apps, e-commerce websites, emails, blog posts, etc. Learning NLP will not only allow you to land high-paying jobs but will also help you in developing your profile for one of the most in-demand jobs in the field of Data Science. If you are searching for how to prepare for an NLP interview or a comprehensive list of NLP interview questions and answers, you have landed on the right page. We have compiled a list of basic, intermediate, and advanced NLP interview questions and answers that can give you a head start in the interview preparation for Data Science, NLP Engineers, and ML Engineers
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Clear allBeginner
Syntactic Processing comes after Lexical Processing in an NLP pipeline. The term syntax means the arrangement of words and phrases to create well-formed sentences in a language. Syntactic Analysis checks if the sequence words or phrases in your text conforms with the rules of formal grammar.
Tasks like POS tagging and dependency parsing are a part of this step. For example, in the sentences “My cat ate its third meal” and “My third cat ate its meal” we can identify that cat is subject and meal is the object of both the sentences. But in the first sentence, “third” is an adjective which is associated with the object whereas in the second sentence it is associated with the subject. Although Syntactic Processing gives us better features to work with, it fails in more complex tasks like machine translation or question answering system as it is unable to understand the underlying meaning of the text.
For example, if you ask a question like “Who is the PM of India?”, it may not be able to give you an answer because ‘PM’ is not a valid English word and Syntactic Processing cannot understand that ‘PM’ in English is an acronym for “Prime Minister”.
Semantic Analysis is the final step of preprocessing in an NLP pipeline. The term semantic means the meaning and interpretation of words in a language. Thus, Semantic Analysis includes the understanding and interpretation of words in a sentence. Tasks like named entity recognition and generating word vectors, sentence vectors and document vectors are a part of this step.
For example, if somebody asks the question "Who is the PM of India?" then, performing Semantic Analysis will ensure that the word vectors of PM and Prime Minister are very similar to one another. Thus, our question answering system can pick up the name of India's Prime Minister from its directory
Intermediate
Principal Component Analysis or PCA is a dimensionality reduction or feature extraction technique. It is a statistical process that converts observations containing correlated features into a set of orthogonal, uncorrelated features called “Principal Components” (PC). If the original dataset contains ‘n’ number of features, then PCA will create ‘n’ Principal Components. Consider this example given below:
In the above example, Figure 1 shows 2 features X1 and X2 in the original dataset. PCA will try to find directions that can capture as much variance as possible from the original data. Hence, once the algorithm is run, the two Principal Components, Z1 and Z2 are shown in Figure 2. Given below are the properties of these Principal Components:
- They are a linear combination of original features in the dataset.
- All Principal Components are independent of each other i.e., their correlation is zero.
- The amount of information (variance) stored in each Principal Component decreases from 1 to ‘n’ i.e., the first Principal Component carries the most amount of information and last Principal Component carries the least amount of information.
In case of NLP when we use feature extraction techniques like BOW, TF-IDF etc., it results in a high dimensional dataset. PCA can help us identify the main Principal Components that carry maximum amount of information. We can then project the original data on the new principal components to get a lower dimensional dataset.
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How to Prepare for an NLP Interview?
Apart from the natural language processing interview questions discussed here, you can follow the below roadmap to fully prepare for an NLP interview:
- Start with the basics, practice the simple text preprocessing techniques such as Tokenization, Stemming, Lemmatization, regular expressions etc. that form the majority of interview questions in NLP.
- Next practice some intermediate techniques like BOW, TF-IDF and n-grams.
- Get used to working with word embeddings. Use pre-trained word embeddings GloVe, Word2Vec etc.
- Train some simple Machine Learning models using the above techniques to get a handle on how to solve various NLP tasks.
- Learn about neural network based architectures such RNN, LSTM and GRU.
- Practice creating custom word embeddings by using encoder networks, CBOW, Skip-Gram and other such architectures.
- Use deep learning python libraries such as TensorFlow and PyTorch to practice neural network based model architectures.
- And finally, learn about the Transformer based model architectures such as BERT, GPT and such.
Natural Language Processing (NLP) Interview Preparation Tips and Tricks
The task of learning NLP is a huge mountain to climb. You can use the below mentioned tips to make your preparation a little easier:
- Don’t be in a hurry to learn. Some of the concepts are not that easy to understand in one go. So, give each topic sufficient time.
- Read the documentation of all the libraries that are popularly used. The documentations contain excellent examples in which you can apply any algorithm that you are looking for.
- If you are a fresher, use Kaggle to read and understand how the experienced programmers approach towards any NLP problem. Reading their notebooks will give you great insights towards how to break down each problem and what techniques works best in various scenarios.
- Develop a habit of participating in hackathons. These competitions are a great place to showcase your learning and get an understanding of where you stand in the competition.
- Don’t hesitate to ask questions. The more you ask, the more you learn. You can check NLP course to further help you with your preparation.
NLP Job Roles
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Research Engineer – NLP
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AI/ML Architect
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Machine Learning Engineer - NLP
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Data Scientist – NLP
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Data Science Manager – NLP
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Software Engineer – NLP
Top Companies
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Wells Fargo
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Adobe
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Mindtree
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Quantiphi
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Harman
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Mercedes Benz
What to Expect in a Natural Language Processing Interview?
When stepping into an NLP interview, prepare yourself for the below topics:
- In technical round, you will be asked theoretical questions around the basics and advance topics of NLP. Make sure to go through this blog before your interview to practice NLP basic interview questions.
- You will be asked to showcase your programming skills. Expect some basic DSA questions. Also practice implementation of all the elements of an NLP pipeline. So it a good idea to practice major NLP coding interview questions.
- You might also be asked to solve a case study using machine learning.
- Prepare your resume well. If you have mentioned any courses, trainings, or certificates, questions around those can also be asked. Don’t put anything in there that you don’t know.
Summary
Congratulations on making it to the end of this blog. If you’ve made it this far then this certainly means that you’re committed to your preparation for a full-time Data Science or NLP role. We certainly hope that these top NLP interview questions can serve as a helping hand in your preparation for all types of data science interviews. For a much deeper understanding, we highly recommend that you check out our popular online course for Data Science. This course takes you through the entire journey of being a professional data scientist with practical data science interview questions and a hands-on problem-solving experience.
Just to recap, in the basic NLP interview questions, we have covered topics around the lexical processing techniques such as tokenization, Bag of Words, TF-IDF, regular expressions, and simple machine learning algorithms which are popularly used in NLP. In the intermediate NLP questions, we have focused on dimensionality reduction techniques such as PCA and LDA. We have also covered questions related to word embeddings, performance metrics and some NLP coding interview questions as well, this includes spacy interview questions as well as NLTK interview questions.
The basic and intermediate NLP interview questions are more than enough to get you through generic data science interviews. For NLP engineer interviews, questions from advanced section will help you out. In the advanced section, we have focused on asking questions about the popular RNN architectures, NLP transformer interview questions, and BERT interview questions as well.
