As an IT professional, you may want to upskill in relevant technologies,
and the best way is to register for online courses. If Artificial Intelligence
(AI) and its applications fascinate you, learning AI, Machine Learning (ML),
and Natural Language Processing (NLP) would be the natural progression to a
lucrative career. You may like to design intelligence applications and models
to solve various business problems.
NLP is increasingly being used along with AI and ML in various use cases
across industries. The Google Translate application is one such use of NLP. As
a subdomain of AI, NLP enables a machine to understand how humans speak and
write in their everyday lives and leverage it for customer satisfaction and
With NLP gaining popularity because of task automation and cognizance of man- to-machine translation, NLP is here to stay for a long time. So consider the option to become an NLP Research Engineer, Machine Learning Engineer, an AI Engineer, or Data Scientist and explore given the vast opportunities in these career domains.
Here is a list of NLP research engineer interview questions that will
help you ace NLP Interviews for a job profile requiring NLP knowledge.
Natural Language Processing (NLP) is an application that helps the
machine understand natural languages and extract the necessary information for
the desired action. It gives computers the ability to comprehend text and
spoken words in the same way human beings can.
NLP deals with naturally processing the text, as in what was said. NLU
or Natural Language Understanding does just that, extracts the context and
intent, i.e. fathoms what was meant.
NLP takes voice commands in the literal sense, but NLU uses intelligence
to draw the inference that the user meant.
NLP can process text from grammar, structure, typo, and point of view,
but NLU helps the machine to surmise the intent behind the language text. And
this is what sets them apart.
Common applications are:
✔ Email filters.
✔ Smart Assistants
✔ Voice Search
✔ Predictive text
✔ Language translation
✔ Auto-completion in Search Engines
✔ Text classification
✔ Question Answering
Text preprocessing is the first step in the process of building a
model. It is a method for cleaning and preparing text data to make it
usable, predictable, and analyzable for a specific task.
The three major types are:
Tokenization: It is the process of dividing groups of texts into
smaller chunks or tokens. For instance, paragraphs are tokenized
into sentences, and sentences are tokenized into words.
Normalization: The database is converted into a series
of normal forms to normalise the data and make the Machine Learning
algorithm simpler. For instance, converting all words to lowercase.
Noise Removal: It is a process of cleaning up the text
by removing unnecessary characters, such as white spaces, special
Some common Text Preprocessing instances are:
✔ Removal of HTML tags,
✔ Removal of stop-words,
✔ Removal of numbers,
✔ Lower casing all letters,
The major components of NLP are:
Entity extraction: It involves slicing a sentence to identify and
extract entities, such as persons, locations, events, etc.
Syntactic analysis: It refers to the logical meaning assigned to
sentences or parts of sentences. These factors and grammar rules define the
correctness of the sentences.
Pragmatic analysis: This involves extracting information from
external documents or queries, using linguistic and logical tools.
It is a piece of software that reads texts in any given language and
assigns parts of speech to each word, such as noun, verb, adjective, etc. Also
called grammatical tagging, it uses an algorithm to categorize word terms
in text bodies, corresponding to a particular part of speech, based on the
definition and its context.
NLP is a subset of AI technology that identifies, understands, and
interprets the request of users in the language format. CI is a user interface
that mixes voice, chat, and a natural language with images, videos, or buttons.
NLP focuses on what the user says in a particular concept.
Conversational Interface provides a more personalized interface for users but
nothing beyond that.
Dependency Parsing is also known as Syntactic Parsing. It is the
process of recognizing a sentence and assigning a syntactic structure to it,
such as the parse tree generated using parsing algorithms. Dependency Parsing
is applied for tasks of grammar checking or semantic analysis.
Latent Semantic Indexing (LSI), also called Latent semantic
analysis, is a mathematical method to improve the accuracy of Information
retrieval. It helps discover the hidden(latent) relationship between the
words(semantics) for generating insights into the topics of those words and
It is generally applied to concept searching and automated document
categorization on small sets of static documents. It is used in software
engineering to understand source code, in publishing for text summarization,
search engine optimization, and other applications.
The above is merely a sample of potential interview questions. Learn more, take online courses, and survive
the interview hot seat to land your dream job of becoming an NLP Engineer!