Natural Language Processing Engineer

Natural Language Processing Engineer interview questions:

A Natural Language Processing (NLP) Engineer specializes in developing products that rely on the intelligent processing of human language by computers. This role encompasses various applications, such as creating intelligent tutors, systems for automatic news article summarization, and speech recognition software. An ideal candidate for this role should possess a strong foundation in natural language processing and excel in related fields like machine learning, text mining, information theory, and information retrieval.

During NLP interviews, candidates will be evaluated based on their familiarity with specialized tools and their experience working on projects involving natural language data. This may include expertise in libraries like nltk (Python), Apache OpenNLP, or GATE. Knowledge of linguistics is often a significant advantage in this field, and fluency in one or more foreign languages can be a valuable asset. Due to the technical nature of the role, research skills are also highly important. While computer science is a common background for NLP Engineers, some individuals have successfully approached this role with a linguistics background, particularly if it emphasizes computational linguistics.

Role-specific questions:

Natural language processing

• What is the concept of Part of Speech (POS) tagging, and can you describe the simplest approach to creating a POS tagger that you can envision?
• If you were given a corpus of annotated sentences, how would you go about building a POS tagger from scratch?
• How would you handle unknown words when developing a POS tagger?
• Explain your approach to training a model that distinguishes whether the word “Apple” in a sentence pertains to the fruit or the company.
• What methods would you employ to identify all instances of quoted text in a news article?
• Outline how you would construct a system that corrects text generated by a speech recognition system.
• Define latent semantic indexing and provide examples of its practical applications.
• How would you go about creating a system for translating English text to Greek and vice versa?
• Describe your strategy for building a system that automatically groups news articles based on their subjects.
• What are stop words, and can you illustrate an application in which it’s advisable to remove them?
• How would you design a model for predicting whether a movie review is positive or negative?

Related fields such as information theory, linguistics and information retrieval

• Explain the concept of entropy, and outline your approach to estimating the entropy of the English language.
• Define what a regular grammar is and discuss any distinctions in power between a regular grammar and a regular expression, if any exist.
• What is the TF-IDF (Term Frequency-Inverse Document Frequency) score of a word, and in what context is this metric useful?
• How does the PageRank algorithm function?
• Provide an explanation of dependency parsing.
• What are the challenges associated with constructing and using an annotated corpus of text like the Brown Corpus, and what strategies can be employed to address these challenges?

Tools and languages

• Which tools have you utilized for training NLP models, such as nltk, Apache OpenNLP, GATE, MALLET, etc.?
• Have you been involved in the development of ontologies?
• Are you acquainted with WordNet or other similar linguistic resources?
• Are you proficient in any foreign languages?