Top 10 NLP Interview Questions And Answers for 2021

Pursuing a Career in Natural Language Processing

Career opportunities in NLP, as with machine learning and AI as a whole, are plentiful and growing by the day. Wherever there is a demand, high-paying jobs are sure to follow. Data scientists, machine learning engineers, computational linguists, and other AI and data engineering professionals who typically leverage NLP in their jobs can expect to earn salaries ranging from $82,000 to $175,000, according to recent data. It’s also a technology that’s expected to remain viable well into the future. 

If you haven’t already mastered it, Simplilearn offers a range of AI and machine learning courses that will get you up to speed on NLP (and so that you can answer those NLP interview questions!) including our focused Natural Language Processing Course. Simplilearn’s unique applied learning model combines the best of online, instructor-led coursework with self-guided videos and hands-on projects that will ensure you’re career-ready upon completion. Best of all, you can access world class skills training from the comfort of your own home at times that best fit your busy schedule.

Even if you’re confident in your skills and training, it’s important to review the types of NLP interview questions that are likely to come up during a job interview. We have compiled some of the most frequently asked NLP interview questions, below, to help you prepare for the next exciting chapter in your career.

1. What Is Natural Language Processing?

While this may sound like a softball NLP interview question, the way you answer it will clue the interviewer into how well you grasp NLP as a whole.  

Natural language processing (NLP) is an automated way to understand or analyze the nuances and overall meaning of natural language, extracting key information from typed or spoken language by applying machine learning algorithms. Since meaning is largely derived from its context, NLP seeks to understand language beyond the literal and allow machines to learn through experience.

2. What is an NLP pipeline, and what does it consist of?

Generally, NLP problems can be solved by navigating the following steps (referred as a pipeline):

• Gathering text, whether it’s from web scraping or the use of available datasets
• Cleaning text (through the processes of stemming and lemmatization)
• Representation of the text (bag-of-words method)
• Word embedding and sentence representation (Word2Vec, SkipGram model)
• Training the model (via neural nets or regression techniques)
• Evaluating the model
• Adjusting the model, as needed
• Deploying the model

3. What does “parsing” mean in the world of NLP?

To “parse” a document, in the context of NLP, is to make sense of its grammatical structure. For example, an NLP application parses text by determining the relationship of words and phrases within the text (e.g., which words are the subject, or object, of a given verb?). Parsing will differ from one set of text to another, since its goal is to understand the grammar and what the writer or speaker is trying to convey.  

4. What is “named entity recognition”?

This will likely be one of the NLP interview questions you will get. Named entity recognition (NER) is an NLP process that separates out the components of a sentence to summarize it into its main components, similar to sentence diagramming in grade school. For example, the sentence “Bob moved to New York City in 1997” may be categorized as:

• Bob = name
• New York City = city/location
• 1997 = time

NER helps machines understand the context of the document by identifying data related to “who, what, when, and where.” It’s very useful for scanning documents and responding to chatbots in a customer service environment.

5. What is a “stop” word?

Articles such as “the” or “an,” and other filler words that bind sentences together (e.g., “how,” “why,” and “is”) but don’t offer much additional meaning are often referred to as “stop” words. In order to get to the root of a search and deliver the most relevant results, search engines routinely filter out stop words.

6. What is “feature extraction” and how is it accomplished using NLP?    

The process of feature extraction involves the identification of certain key words or phrases that put it into a particular category, often based on the author’s purported sentiment. For example, a product review by a customer that uses the word “great” or the phrase “good quality” could be summarized as a positive review. The feature extraction process in NLP could enable a given phrase or use of certain words to be “tokenized” into the positive review category.

7. How do you test an NLP model? What metrics are used?

NLP models should be tested for accuracy, but also must consider the likelihood of false positives and false negatives due to the complexity and nuances of language. Therefore, while accuracy is important, you also want to test an NLP model using the following metrics:

• Recall. This is expressed by the following equation: 

True Positive / True Positive + False Negative = True Positive / Total Actual Positive

• Precision. This is expressed by the following equation:

True Positive / True Positive + False Positive = True Positive / Total Predicted Positive

• F1. This is a combination of recall and precision and is expressed by the following equation:

F1 = 2X Precision * Recall / Precision + Recall

8. What are two applications of NLP used today?

There are several real-world NLP applications in use today, including:

9. What is “term frequency-inverse document frequency?”  

Term frequency-inverse document frequency (TF-IDF) is an indicator of how important a given word is in a document, which helps identify key words and assist with the process of feature extraction for categorization purposes. While “TF” identifies how frequently a given word or phrase (“W”) is used, “IDF” measures its importance within the document. The formulas to answer this NLP interview question are as follows:

• TF(W) = Frequency of W in a document / Total number of terms in the document
• IDF(W) = log_e (Total number of documents / Number of documents having the term W)

Using these formulas, you can determine just how important a given word or phrase is within a document. If the TF-IDF is high, then the frequency of that term is lower; if the TF-IDF is low, then its frequency is higher. Search engines use this to help them rank sites.

10. What is “latent semantic indexing?”  

Undoubtedly, this will be one of the NLP interview questions that you will be asked: What is latent semantic indexing? Latent semantic indexing (LSI) is used to extract useful information from unstructured data by identifying different words and phrases that have the same or similar meanings within a given context. It’s a mathematical method for determining context and obtaining a deeper understanding of the language, widely used by search engines.