Natural Language Processing Engineer Position Overview

Companies are fiercely competing amongst themselves to get the most of customers and provide them with the best services possible. For this purpose, Natural Language Processing and Text Analysis have become more important than ever. The outcome of natural language processing is significant to businesses as it extracts true emotions behind the texts generated by common users. Based on these insights, enterprises take appropriate actions to improve their customer services.

A Natural Language Processing Engineer develops Text Analytics strategies and tools, with a focus on sentiment analysis. These professionals understand the current challenges of text analytics within the company, undertake a regular analysis of customers’ feedback, and present an analysis report to the product team for review.

Qualifications to look for:

• Minimum academic level required: B.Sc. in Computer Science, Computational Linguistics or related fields
• Ideal academic level preferred: M.Sc./PhD in Computer Science, Computational Linguistics or related fields

Skills to look for:

• Solid Java and Python development knowledge
• Strong knowledge of Sentimental Analysis, Natural Language Understanding, and Natural Language Generation
• General software development skills
• Communication skills
• Excellent reasoning ability

Enlisted below are some Natural Language Processing Engineer interview questions every recruiter must ask to find the most proficient candidate.

Job-specific interview questions

Natural Language Processing

• Do you know about latent semantic indexing? Where can you apply it?
• Is it possible to find all the occurrences of quoted text in an article? If yes, explain how?
• What is a POS tagger? Explain the simplest approach to build a POS tagger?
• Which is a better algorithm for POS tagging – SVM or hidden Markov models?
• What is the difference between shallow parsing and dependency parsing?
• What package are you aware of in python which is used in NLP and ML?
• Explain one application in which stop words should be removed.
• How will you train a model to identify whether the word “Raymond” in a sentence represents a person’s name or a company?

Interview Questions Related To Other Fields:

• Differentiate regular grammar and regular expression.
• How will you estimate the entropy of the English language?
• Describe dependency parsing?
• What do you mean by Information rate?
• Explain Discrete Memoryless Channel (DMC).
• How does correlation work in text mining?
• How to calculate TF*IDF for a single new document to be classified?
• How to build ontologies?
• What is an N-gram in the context of text mining?
• What do you know about linguistic resources such as WordNet?
• Explain the tools you have used for training NLP models?