Whether you are a Data Scientist or an AI engineer, we are sure you must be fascinated with Natural Language Processing (NLP). One of the most popular subset of Data Science, NLP has been around for quite some time now. However, as the demand for Data Science and AI increased across industries, much attention has been given to NLP in recent years. From language translation to chatbots to smart assistants, NLP finds its use across multiple and interesting businesses.
Serving your interests you might be well read on NLP. But to crack a Data Scientist role where you specifically work on NLP and its applications requires focused knowledge and preparation. One of the best ways to prepare is to practice topic-wise interview questions. In this article we share with you the list of top 20 questions on NLP that will help you crack a Data Scientist and AI role. We earlier shared with you 25 commonly asked interview questions on Deep Learning.
Before we begin, let’s briefly understand what NLP is.
In layman terms, Natural Language Processing, popularly known as NLP focuses on understanding and manipulating the human language by computers.
We are well aware that today the world produces huge amounts of data. And a large part of it is produced in the form of text. NLP helps in understanding, analysing the human language in these texts and further use it in building algorithms and programming computers. By doing so, NLP helps computers communicate with humans in the human language.
One of the most popular examples of this is Alexa. Whenever you address Alexa, it records your words which are sent to Amazon servers for interpretation, of which based on words which make the most sense, Alexa performs the action or gives the required response. While this is presented in the simplest possible way, the entire process is complex and requires high computational power.
Top 20 Interview Questions on Natural Language Processing
- What do you understand about NLP?
- Define stop words
- Explain the process of parsing.
- Explain the difference between NLP and NLU.
- Define pragmatic analysis
- Explain the step by step process to solve a NLP problem
- What do you understand about F1 score in NLP?
- Explain the process of feature extraction
- What is Latent Semantic Indexing?
- Name some popular python libraries for NLP
- Define text summarization in NLP
- Name some tools for training NLP models
- What is the use of TF-IDF?
- What do you understand by pragmatic ambiguity in NLP?
- Define part of speech (POS) tagging
- Explain Natural Language Generation
- What is the difference between semantic and syntactic analysis in NLP?
- Name some open source libraries for NLP
- What do you mean by n-gram in NLP?
- Define a GloVe in NLP.
We hope you found this article useful. Prepare these NLP questions before your next Data Science interview. We’re sure these will help you crack your dream role.
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