15 NLP Interview Questions

  • Bhumika Dutta
  • Sep 10, 2021
  • NLP
15 NLP Interview Questions title banner

Introduction

 

The popularity of Artificial Intelligence is rising every day due to the vast availability of data and the advancement of the field in performing tasks that are otherwise very difficult manually. In fact, AI has now made our lives way easier than before. One of the subfields under machine learning that is being practiced in many sectors now is Natural Language Processing (NLP). 

 

Now, what actually is Natural Language Processing? It is a linguistics component of Artificial Intelligence that uses software to assist a computer read or modify natural language written or spoken by people. NLP combines Machine Learning, Deep Learning, and statistical models, and it is one of the most rapidly growing technologies due to the enormous availability of Big Data, powerful equipment, and algorithms. 

 

(Must read: Statistical data distribution models)

 

The field of NLP has one of the most promising careers for people with technical backgrounds as it is innovating every day and spreading its impact in many sectors. There are many applications of NLP that are worth noting. 

 

To sit in any NLP interview, one must be well versed with some particular topics along with the basics of artificial intelligence and NLP. In this article, we are going to list out the most asked interview questions of NLP. 


 

NLP interview questions:

 

  1. Name two popular applications of Natural Language Processing.

 

NLP has many real-life applications, two of the most popular ones are:

 

  • Chatbots: Companies have begun to use chatbots for 24/7 service to give better customer assistance. Customers' fundamental questions are answered via chatbots. If a chatbot is unable to handle a client's query, it sends it to the support staff while continuing to engage the consumer. It gives clients the impression that the customer service team is responding fast. 

 

Companies have been able to establish pleasant relationships with customers thanks to chatbots. Natural Language Processing is the only way to make it happen.

 

  • Google Translate: One of the most well-known uses of Natural Language Processing is Google Translate. It assists in the translation of written or spoken phrases into any language. We may also use Google Translate to determine the right pronunciation and meaning of a word. It achieves success in translating sentences into multiple languages by employing sophisticated Natural Language Processing methods.

 

(Also check: NLP Guide For Beginners)

 

  1. What is NLTK?

 

Natural Language Toolkit (NLTK) is a python library that processes natural language and extracts data from it for computers. To comprehend natural languages, we may use NLTK to perform techniques like parsing, tokenization, lemmatization, stemming, and more. It aids in text categorization, linguistic structure parsing, document analysis, and other tasks.

 

Some of the most common NLTK packages are DefaultTagger, UnigramTagger, treebank, wordnet, patterns, SequentialBackoffTagger, and so much more. 

 

 

  1. What does an NLP pipeline consist of?

 

NLP uses pipelines to understand the natural language of humans and the following are the processes of an NLP pipeline:

 

  • Text gathering(web scraping or available datasets)

  • Text cleaning(stemming, lemmatization)

  • Feature generation (Bag of words)

  • Embedding and sentence representation(word2vec)

  • Training the model by leveraging neural nets or regression techniques

  • Model evaluation

  • Making adjustments to the model

  • Deployment of the model.

 

 

  1. What is the process of feature extraction in NLP?

 

In any sentence, the features are used to conduct semantic analysis or document classification. A typical paradigm for feature creation is the bag of words. A phrase can be tokenized, and then a group or category can be created from these individual words, which can then be further examined or exploited for specific features (number of times a certain word appears, etc). 

 

Other than the bag of words, latent semantic indexing and word2vec are also popular models for feature extraction in NLP. 

 

 

  1. What is Syntactic Analysis?

 

Syntactic analysis is a method of examining sentences in order to determine their meaning. A machine can examine and comprehend the order of words in a phrase using syntactic analysis. NLP uses a language's grammar rules to aid in the syntactic analysis of word combinations and order in documents.

 

(Suggested article: Applications of NLP)

 

 

  1. What are the techniques used for syntactic analysis?

 

The following diagram shows the techniques of syntactic analysis:


Parsing, word segmentation, morphological segmentation, stemming, lemmatization are the Techniques of Syntactic Analysis.

Techniques of Syntactic Analysis (source)


  • Parsing: Parsing is used to decide the structure of the text in any document and analyze it on the basis of the grammar used.

  • Word Segmentation: In the second step, the text is segregated into smaller units.

  • Morphological segmentation: The goal of morphological segmentation is to deconstruct words into their simplest form.

  • Stemming: It removes the suffix from any word to obtain the root word.

  • Lemmatization: It allows you to mix words with suffixes without changing their meaning.

 

(Recommended blog: NLP techniques for feature extraction)

 

 

  1. What do you mean by LSI?

 

The mathematical method of latent semantic indexing is used to increase the accuracy of the information retrieval process. Machines can identify the hidden (latent) link between meanings thanks to the design of LSI algorithms (words). Machines produce numerous ideas that are associated with the words in a phrase to improve information comprehension. 

 

Singular value decomposition is a technique used to interpret information. It is commonly used to manage both structured and unstructured data. Singular value decomposition is a technique used to interpret information. It is commonly used to manage both structured and unstructured data.

 

 

  1. What is the significance of TF-IDF?

 

TFIDF (term frequency-inverse document frequency) stands for term frequency-inverse document frequency. TFIDF is a numerical statistic used in information retrieval to indicate how significant a word is to a document in a collection or group of collections.

 

 

  1. What is Lemmatization?

 

Lemmatization is a term that refers to doing things correctly using vocabulary and morphological examination of words. The ends of the words are eliminated in this procedure to restore the base word, which is also known as Lemma. 

 

As a result, the major goal of Lemmatization and stemming is to discover and return the sentence's root words in order to investigate different extra information.

 

 

  1. What is Regular Grammar?

 

A regular language is represented by regular grammar. Regular grammar has rules like A -> a, A -> aB, and many others. The rules automate the detection and analysis of strings. There are four tuples in regular grammar:

 

  • The non-terminal set is denoted by the letter ‘N.'

  • The phrase ‘' refers to a group of terminals.

  • The letter ‘P' stands for the production set.

  • The commencement of non-terminal is indicated by the letters’ € N'.

 

(Related reading: Examples of NLP)

 

 

  1. What is the difference between regular grammar and regular expression?

 

Regular Grammars have four tuples (N, P, S € N). N indicates for the non-terminals' sets, T stands for the terminals' set, P stands for the set of productions to alter the start symbol, P has its productions from one of the kinds, and finally, S stands for the start non-terminal.

 

Regular expressions, on the other hand, are a set of characters that define a search pattern and are commonly used in pattern matching or string matching.
 

 

  1. What are the terminologies in NLP?

 

The following are the terminologies in NLP:

 

  1. Weights and Vectors

 

  • Use of TF-IDF for information retrieval

  • Length (TF-IDF and doc)

  • Google Word Vectors

  • Word Vectors

 

 

  1. Structure of the Text

 

  • POS tagging

  • Head of the sentence

  • Named Entity Recognition (NER)

 

 

  1. Sentiment Analysis

 

  • Knowledge of the characteristics of sentiment

  • Knowledge about entities and the common dictionary available for sentiment analysis

 

 

  1. Classification of Text

 

  • Supervised learning algorithm

  • Training set

  • Validation set

  • Test set

  • Features of the text

  • LDA

 

 

  1. Machine Reading

 

  • Removal of possible entities

  • Joining with other entities

  • DBpedia

 

 

  1. What is the main difference between NLP and NLU?

 

The difference between Natural Language Processing and Natural Language Understanding is as follows:

 

Natural Language Processing

Natural Language Understanding

NLP is used to produce technologies that help in better communication between humans and computers.

NLU techniques are used to solve complex programs that are related to machine understanding.

NLP takes care of all the processes that are required for the interaction between computers and humans

NLU helps in converting the unorganized data into structured data, for the machines to understand.

 

(Read also: Introduction of LSA and LDA)

 

 

  1.  What is tokenization in NLP?

 

The goal of natural language processing is to teach computers how to analyze huge quantities of data in natural language. In NLP, tokenization refers to the process of breaking down a text into individual tokens. 

 

A token in the shape of the word can be imagined. A sentence is formed in the same way that a word is formed. Splitting the text into minimum units is a key step in NLP.

 

 

  1. What is Pragmatic Analysis and Pragmatic Ambiguity?

 

In NLP, pragmatic analysis is a crucial job for understanding knowledge that exists outside of a given document. The goal of using pragmatic analysis is to concentrate on a specific feature of a document or text in a language. This necessitates a thorough understanding of the real world. The pragmatic analysis helps software programs to know the true meaning of phrases and words through critical interpretation of real-world data.

 

Multiple descriptions of a word or a sentence are referred to as pragmatic ambiguity. When the meaning of a statement is unclear, it is called ambiguity. The meanings of the sentence's words may vary. 

 

As a result, understanding the meaning of a sentence becomes a difficult challenge for a computer in practice. As a result, pragmatic uncertainty emerges.

 

(Similar Read: 20 Data Science Interview Questions)


 

Conclusion

 

NLP is a very interesting field of study as it is rapidly advancing every day and producing many innovative technologies under it. For an interview for NLP, one must master the basics of Machine learning and artificial intelligence along with natural language processing. They should also have a good grasp of the Python programming language. This article covers 15 frequently asked questions of NLP in an interview and one must go through these topics to crack the job. 

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