Introduction to NLP Interview Questions and Answers
NLP stands for Natural Language Processing. It is one of the big planning of multiple language processing by utilizing computer science, engineering knowledge, especially information engineering knowledge and strong artificial intelligence, which make sure proper interaction between human languages and computer systems.
If you are looking for a job related to NLP, you need to prepare for the 2021 NLP Interview Questions. Every interview is indeed different as per the different job profiles. Here, we have prepared the important NLP Interview Questions and Answers, which will help you succeed in your interview.
In this 2021 NLP Interview Questions article, we shall present the 10 most important and frequently asked NLP Interview questions. These questions are divided into two parts are as follows:
Part 1 – NLP Interview Questions (Basic)
This first part covers basic Interview Questions and Answers.
Q1. Explain in details about Natural Processing language (NLP), which is currently one of the key artificial language learning processes that have been started in the industry?
Answer:
Natural Language Processing (NLP) is designed for understanding and analyzing the natural languages automatic way, and export data or possibly require information from those available data. NLP has defined an algorithm that helps mainly with machine learning. This kind of machine learning algorithm actually helps for understanding analyzing some of the natural languages.
Q2. There have some various common elements of natural language processing. Those elements are essential for understanding NLP properly. Can you please explain the same in detail with an example?
Answer:
There have a lot of components normally using natural language processing (NLP). Some of the major components are explained below:
- Extraction of Entity: It identifies and extracts some critical data from the available information, which helps to segment of provided sentence on identifying each entity. It can help identify one human that it’s fictional or real, the same kind of reality identification for any organization, events, or any geographic location, etc.
- The analysis in a syntactic way: it mainly helps for maintaining ordering properly of the available words.
- An analysis in a programmatic way: It is one of the key processes of NLP. It helps for extracting data from the specifically available text in natural languages.
Let us move to the next NLP Interview Questions.
Q3. Explain details about varieties areas available in processing natural languages smartly, whether we know impacted areas are very small as this processing started very recently?
Answer:
Natural language processing (NLP) can have an implementation in various areas of the current industry environment. Some of the key areas are explaining below:
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- An analysis was done semantically.
- Summarize natural language information automatically.
- Classification of varieties text is written in natural language.
- The ready answer to some common questions
We can give some key examples of real-life where natural language processing (NLP) used broadly. Examples are Google Assistance, IOS Siri, or Amazon echo.
Q4. In the case of processing natural language, we normally mentioned one common terminology, NLP and binding every language with the same terminology properly. Please explain in details about this NLP terminology with an example?
Answer:
These are the basic NLP Interview Questions asked in an interview. There have several factors available in case of explaining natural language processing. Some of the key factors are given below:
- Vectors and Weights: Google Word vectors, length of TF-IDF, varieties documents, word vectors, TF-IDF.
- Structure of Text: Named Entities, tagging of part of speech, identifying the sentence’s head.
- Analysis of sentiment: Know about the sentiment features, entities available for the sentiment, sentiment common dictionary.
- Classification of Text: Learning supervising, set off a train, a set of validation in Dev, Set of define test, a feature of the individual text, LDA.
- Reading of Machine Language: Extraction of the possible entity, linking with an individual entity, DBpedia, some libraries like Pikes or FRED.
Q5. One another very common terminology used in the case of natural learning processing is called TF-IDF. Please explain in details on the understanding of TFIDF properly and come with some example?
Answer:
TF-IDF or tf-IDF is basically stood for some critical frequency of term or some inverse frequency of a specific document. TF-IDF is basically using for identifying some of the keywords from an entire document written in natural language. It mainly involves retrieving information from the critical document by using some statistical numeric data for identifying some of the keywords and mentioning how much important that word is specifically in the collection of multiple documents or in the set of collections.
Part 2 – NLP Interview Questions (Advanced)
Let us now have a look at the advanced Interview Questions.
Q6. There are several tagging using for processing natural languages. In all those tagging parts of speech (POS), tagging is one of our industry’s popular ones. Please explain in detail about the part of speech (POS) tagging and how it can be used properly?
Answer:
Part of speech tagger is a very interesting and most important tool for properly processing natural language. This part of speech (POS) tagger is a normal tool or software which helps for reading some critical text independent of any languages, then assign entire sentence in part of speech for each word or some other tokenization logic define in the software, such as adjective, verb or noun etc.
It normally holds some specific algorithm that helps to label some of the terms in the entire text body. It has some varieties of categories which are more complex than define above utility. The above define functionality is one of the very basic features of the POS tag.
Q7. As the analysis is one of the critical requirements of natural language processing (NLP), we can follow several analysis approaches for understanding NLP properly. In between all those, one of the key analysis called Pragmatic Analysis. Please explain about Pragmatic analysis in details?
Answer:
Pragmatic analysis is one of the critical analysis defines in NLP. It is mainly handling some knowledge which is belonging to the outside world. That means some of the knowledge which always be external for some define documents or already queries. This kind of analysis mainly concentrates on the critical interpretation of some specific word and tries to understand its actual meaning. For doing this kind of analysis, real-world knowledge is very much required.
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Q8. Again, as NLP is used for multiple language processing smartly and interacting with a computer system based on proper language understanding, one of the key parsings normally used by NLP is dependency parsing. Please explain dependency parsing in details with proper explanation?
Answer:
Dependency parsing is actually known in the industry as syntactic parsing. It is doing one of NLP processing’s critical tasks,t identifying or recognizing some of the sentences and then assigning those in some define a syntactic structure for understanding properly. One of the popular syntactic structures is parsed tree define with some parsing algorithm.
Q9. One of the very basic requirement of NLP is keyword normalization. There have normally two process or techniques followed by NLP for handling proper keyword normalization. Please explain in details about keyword normalization and which techniques can be followed for the same.
Answer:
This is the most asked NLP Interview Question in an interview. There has two key normalization processes in NLP, which help for keyword normalization. These two processes are Stemming and Lemmatization.
Q10. There have some classification model define in NLP. What kind of features can be followed by NLP for improving accuracy in the classification model?
Answer:
There have several classifications followed by NLP, explaining the same below:
- Counting frequency of defined terms.
- Notation of vector for every sentence.
- Part of Speech (POS) tagging.
- Grammatical dependency or some defined dictionary or library.
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