Natural Language Processing Engineer

Natural Language Processing Engineer qualifications to look for 

A Natural Language Processing (NLP) Engineer develops products that process human language by a computer. 

Your next top candidate should be well-versed in related fields such as machine learning, text mining, information theory, and information retrieval. 

Look for candidates who have experience with projects working with natural language data such as IBM Watson Tone Analyzer, NLTK (Python), Apache OpenNLP or GATE. Some of your brightest stars in the field will also have knowledge of linguistics and/or fluency in one or more foreign languages. 

Computer science is the most relative background for this role, but some top recruits may have a linguistics background that emphasizes computational linguistics. 

Top tip: Hire candidates willing to grow by making sure their personal career goals align with your company's mission.

Natural Language Processing interview questions

Natural language processing questions

• We need you to build a system that automatically groups news articles by subject. Walk me through the process.
• How would you train a model that identifies whether the word “Amazon” in a sentence belongs to the region or the company?
• We need you to design a model to predict whether a movie review was positive or negative. What is your process? 
• Explain what part of speech (POS) tagging is used for. What is the simplest approach to building a POS tagger that you can imagine?
• You need to build a POS tagger from scratch given a corpus of annotated sentences. How do you approach this? How would you deal with unknown words?
• You need to find all the occurrences of quoted text in a news article. How do you approach this? 
• We need you to build a system that auto-corrects text generated by a speech recognition system. Walk me through this. 
• Explain latent semantic indexing. Where can it be applied?
• How would you build a system to translate English text to Spanish? How do you reverse the translation? 
• Explain stop words. When should you remove stop words in an application? 

Information theory, linguistics and information retrieval questions

• What things would you need to troubleshoot when building and using an annotated corpus of text such as the Brown Corpus? 
• Define entropy. How would you estimate the entropy of the Spanish language?
• Explain regular grammar. Does this differ in power to a regular expression and if so, in what way?
• Define the TF-IDF score of a word. When is this useful?
• Explain how the PageRank algorithm works.
• Define dependency parsing.
  

Tools and languages questions

• Are you fluent in any foreign languages?
• Explain any tools for training NLP models (Apache OpenNLP, NLTK, GATE, MALLET, etc.) that you’ve used.
• What is your experience building ontologies?
• Explain WordNet or other related linguistic resources you’ve used.