tf idf interview questions

This blog has nlp interview questions. If you’re looking for jobs as a nlp research engineer, read through these nlp interview questions that will help you. Last Updated: 25 Nov 2022.

Here is a list of interview questions and their responses for NLP research engineers that will enable you to ace any NLP interview. For your convenience, the NLP interview questions have been broken down into subgroups. So purchase your time tickets and make the giant step toward obtaining your dream position as an NLP Engineer.

The majority of people begin their mornings with a brisk walk and some grocery shopping. They quickly pull out their phone in the grocery store if there is an item they want to find but the packaging is written in a language they cannot read. They launch the Google Translate app, and presto, they can choose whether to purchase the item or not. People living abroad and those with certain allergies will benefit greatly from the app.

An outstanding illustration of Natural Language Processing (NLP) software is the Google Translate app. And if you’re inspired to create something more intelligent by these apps, NLP is the field for you. Making a machine (a computer) understand how people use language in their daily lives is the goal of NLP, a branch of artificial intelligence.

Because it can automate tasks for organizations and save them time, NLP has recently grown in popularity. It is an innovative technology that will likely endure for a very long time. Given the numerous opportunities, it would be a great idea to think about pursuing a career as an NLP Research Engineer, Data Scientist, or Machine Learning Engineer.

Explain TF-IDF | Data Science Interview Questions Series

The TF-IDF statistic quantifies a word’s significance to each document in a corpus or collection. It is the result of two statistics: inverse document frequency and term frequency. A term’s frequency in a document is measured by its term frequency, and a term’s frequency in a collection of documents is measured by its inverse document frequency.

Word vectors are a type of word representation that makes it possible to understand that words with similar meanings have a similar representation. Several NLP tasks, such as document classification, clustering, and information retrieval, can benefit from this. We can more easily compare and contrast word meanings and categorize them by doing so by representing words in a vector space.

When a document’s most crucial terms are the main focus, a straightforward term frequency model can be used without taking into account how frequently those terms appear in other documents. This can be helpful, for instance, when writing a document summary or looking for documents that are similar to a specific document.

A statistical technique called TF-IDF is used to assess a word’s significance within a document. Search engines frequently use this metric to assess how relevant a document is to a user’s query. It is likely that during your interview you will be questioned about working with TF-IDF if you are applying for a position that requires it. In this article, we go over the most typical TF-IDF queries and how to respond to them.

The process of reducing a word to its root or most basic form is called stemming. This is frequently done to distill a word’s meaning down to its most fundamental form. For instance, “stemming” can be shortened to “stem,” its root form. When working with text data, this can be useful because it makes it simpler to compare words that share a root form.

Top NLP Interview Questions

  • What is Naive Bayes algorithm, when we can use this algorithm in NLP?
  • Explain Dependency Parsing in NLP?
  • What is text Summarization?
  • What is NLTK? How is it different from Spacy?
  • What is information extraction?
  • What is Bag of Words?
  • What is Pragmatic Ambiguity in NLP?
  • What is Masked Language Model?
  • What is the difference between NLP and CI (Conversational Interface)?
  • What are the best NLP Tools?
  • Without further ado, let’s kickstart your NLP learning journey.

  • NLP Interview Questions for Freshers
  • NLP Interview Questions for Experienced
  • Natural Language Processing FAQ’s
  • List Some Components Of Nlp?

    Answer: Below are the few major components of NLP.

    a. Entity extraction:

    It entails breaking down a sentence into segments to find and extract entities, such as real or imagined people, organizations, places, things to do, etc.

    b. Syntactic analysis:

    It refers to the proper ordering of words.

    c. Pragmatic analysis:

    It is a step in the information extraction from text process.

    Top 30 NLP Interview Questions and Answers


    Most often, job applicants for positions in natural language processing are unaware of the types of questions they might be asked during the interview. Knowing the fundamentals of NLP is a requirement, but it’s also a good idea to practice for NLP interview questions that might be tailored to the company and what it does. By doing so, you will not only be seen as a good fit for the position, but you will also be well-prepared for the role that you hope to fill.

    The top 30 Natural Language Processing interview questions and responses have been compiled by Intellipaat to assist you in your interview.

    The three sections below represent how we have divided the natural language processing interview questions:


    What is TF-IDF explain with an example?

    TF-IDF is useful in many natural language processing applications. For instance, search engines rank a document’s relevance to a query using the TF-IDF algorithm. Additionally, topic modeling, text summarization, and text classification all use TF-IDF. Be aware that there are various methods for determining the IDF score.

    What is the main purpose of the TF-IDF approach?

    With the aid of TF-IDF, it is possible to quantify the relevance of each word in a document by associating it with a number. Consequently, documents containing comparable, pertinent words will have comparable vectors, which is what we seek in a machine learning algorithm.

    What are two limitations of the TF-IDF representation?

    TF-IDF is constrained in a number of ways, including the following: – It computes document similarity directly in the word-count space, which can be slow for large vocabularies. – It is predicated on the idea that the counts of various words serve as independent proof of similarity. – It makes no use of semantic similarities between words.

    What does TF-IDF tell you?

    Conclusion. A helpful algorithm called TF-IDF (Term Frequency – Inverse Document Frequency) uses the frequency of words to assess how pertinent they are to a given document. It’s a fairly straightforward but intuitive method of weighting words, making it a great starting point for a variety of tasks.

    Related Posts

    Leave a Reply

    Your email address will not be published. Required fields are marked *