The best NLP interview questions to keep a note on

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by Madhurjya Chowdhury


12 October 2021

Artificial intelligence (AI), a large and rapidly evolving field, has the potential to automate basic activities normally performed by people, such as those requiring a mastery of linguistic and conversational intricacies. If you’re reading this, you’re probably already familiar with Natural Language Processing (NLP), a branch of artificial intelligence that allows bots to communicate with people through language.

If you are going for an NLP interview, you must know the answers to the most common NLP interview questions. Here are some frequently asked questions in NLP to give you an idea of ​​what it is about.

1. What is natural language processing?

While this may seem like a simple NLP interview question, the way you answer will reveal how much you understand NLP as a whole.

Natural Language Processing (NLP) is an automated method of understanding or analyzing the intricacies and importance of natural language by using machine learning algorithms to extract important information from written or spoken language. NLP aims to understand language beyond the basics and enable robots to learn from experience, as meaning is formed primarily from its context.

2. What is an NLP pipeline?

When you use natural language processing on text or speech, it turns the entire input into strings, and then the main chain goes through a series of steps (the process called the processing pipeline.) It oversees your input data using trained pipelines and reconstructs the entire chain based on tone of voice or length of phrase.

The element returns to the main chain after each pipeline. After that, it moves on to the next component. Elements, their models and training determine capabilities and effectiveness.

3. What does “parsing” mean in NLP?

In NLP, “analyzing” a document means deciphering its grammatical structure. An NLP program, for example, analyzes text by detecting relationships between the words and the sentences it contains. Because the goal of the analysis is to understand the grammar and what the author is trying to express, this will vary from batch to batch.

4. What is “named entity recognition”?

This will most likely be one of the questions you will be asked during interviews with NLP. Likewise, for creating sentence diagrams in elementary school, Named Entity Recognition (NER) is an NLP technique that separates the elements of a sentence to summarize it into its key elements. For example, the phrase “Sam moved to California in 1999” might be classified as:

  • Sam = name
  • California = city or place
  • 1999 = hour

The NER identifies facts related to “who, what, when and where” to help machines grasp the context of the content. In a customer service context, it’s great for scanning papers and reacting to chatbots.

5. What is a word “stop”?

The words “stop” are articles like “the” or “a” as well as other filler words like “how”, “why” and “is” that tie the sentences together but don’t contribute much to the content. Stop words are frequently filtered by search engines in order to get to the heart of a query and give the most relevant results.

6. What is “feature extraction”?

Feature extraction is the process of identifying important words or phrases that place them in a certain category, usually based on the author’s stated mood. A customer’s rating of a product, for example, may qualify as a favorable opinion if the adjective “excellent” or the term “good quality” is used. The feature extraction method in NLP may be able to “tokenize” a sentence or the use of specific terms in the category of favorable reviews.

7. What is a Turing test?

Alan Turing designed the Turing test, which could tell the difference between people and machines. If a computer machine passes this test using language, it is said to be intelligent. Alan believed that the intellect of a machine could be demonstrated simply by its ability to use language in the same way as humans.

8. Name two applications of NLP that are in use today?

  • Chatbots: Chatbots (powered by NLP) are frequently used to initiate customer service conversations, with the aim of resolving basic consumer questions and directing them to the appropriate staff if the chatbot is unable to do it. Businesses benefit from increased efficiency and cost savings.
  • Online translation: NLP is used by services like Google Translate to transform written and spoken words into different languages, as well as to help with pronunciation.

9. List some Python libraries that you use for NLP.

GenSim, SpaCy, NLTK, Scikit-learn, CoreNLP, TextBlob.

ten. What is the definition of collocation?

A collocation is a set of two or more words that are related and can be used to express something in a traditional way. “A strong breeze”, “the rich and the powerful” and “the weapons of mass devastation”, for example.

11. What is a neural network?

The term “network networks” refers to a set of algorithms used to discover the links between sets of data using a process similar to that of a human brain. These networks can adapt to changes in input, allowing them to achieve the best results without changing the output criteria. Artificial intelligence (AI) and machine learning (ML) technologies are the foundations of neural networks.

The neural network approach is similar to how the human brain works. In this context, a neuron is a mathematical operation that collects data and categorizes it according to a certain design.

Conclusion

NLP is one of the most promising areas for people with technical training as it is constantly evolving and spreading its effect to many industries. There are several applications of NLP that are worth mentioning. To participate in an NLP interview, you must be familiar with a few specific topics as well as the fundamentals of artificial intelligence and NLP. However, the most crucial cycle a candidate should focus on is the interview. However, it will be difficult for you to pass the technical steps if you have no previous experience in solving real world problems.

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