Top 5 most frequently asked NLP interview questions

Artificially intelligent entities in our times are gradually becoming more and more like humans. Thanks to the abundance of relevant data, we can train our models and tools to behave as a human being does. Natural language processing or NLP is such an aspect that renders a machine able to understand human emotions and implications through natural language. NLP as a discipline emerged as a result of an alliance between linguistics, artificial intelligence, machine learning, and deep learning. And the implementations are remarkably successful. Customer engagement tools, chatbots, virtual assistants like Google Home, Alexa, and Siri, voice-based GPS services, speech-to-text, and text-to-speech conversion systems are the most prominent examples of NLP-enabled systems that can understand and follow linguistic instruction.

Human language is spoken and written following a set of rules. And NLP model is formulated by deep learning, statistical, and machine learning tools modelled after these rules. A fully trained NLP tool can understand the latent implications of human language and process the same for maximum effects.

The implementation of NLP is changing the commerce and public service paradigms that we avail all the time and openings for the adept are on the rise. This article will try to discuss some crucial aspects of NLP that are also frequently asked in the form of NLP interview questions.

  1. What are the steps of data preprocessing in NLP?
  • Segmentation of the sentence and word tokenization
  • Normalization, stopping the removal of words, removing punctuations and digits. Complete preservation of textual information.
  • Performing transliteration, code-mixing, and sentiment detection.
  • Parts of speech tagging, parsing, etc.
  • What are text extraction and cleanup?

Text extraction is a paradigm concerned with filtering the text or any nontextual information. Like symbols, markups, and punctuations. And also format the data as per the project requirement.

Purpose of use –

  • Recognition of named Entity
  • Analysis of sentiments
  • Summarization of texts
  • Mining aspects
  • Modeling a topic
  • What are the popular sources for data in NLP?

Today, we live amidst an abundance of data. And there are a plethora of sources for obtaining data by ethical means.

Publicly available sources are a very reliable source of new kinds of data. The public databases are always updated and can be a guide under dynamic circumstances.

Data scraping is a method that is partially dependent on automation. The internet is a source of raw and unstructured data. And a machine learning or deep learning tool can be programmed for fetching the same in certain formats that can be understood by the machines.

Data augmentation is a technique to merge or culminate multiple datasets for the synthesis of new data sets.

  • What is TF-IDF in NLP?

TF-IDF stands for Term Frequency – Inverse Document Frequency. The same is concerned with measuring the weightage or importance of a particular word compared to other words in a manuscript. TF-IDF helps with information retrieval and summarization through a common scoring matrix. Words in this process are considered as a vector added with semantic information. Which adds differential weightage to each vector(word). Utilization frequency and unusual words thus can be used for a plethora of NLP operations.

  • What are parts of speech tagging?

The part of speech tagging algorithm is a tool for analyzing each word and tagging parts of speeches in the same. A POS tagging tool can separate the part of speech and the prefixes or suffixes and makes the same more lucid and understandable for an NLP entity. Eg. “crows” is a word depicting more than one crow. And the word is a noun. So a POS tagger will tag the same with noun & plural.

Conclusion What an employer seeks, is hands-on and relevant experiences. Thus, NLP interview questions are designed to evaluate the concepts and adeptness under a real-world scenario. Therefore, it is recommended for a student to take up projects independently if necessary or join an internship that can help with skill development. When It comes to a job in NLP, no stones should be left unturned. In this article, we tried to share the most commonly asked conceptual NLP interview questions that a freshman is expected to attend. But the realm of linguistic processing is vast beyond imagination. Thus, this write-up is expected to act as a moral boost and an encouraging read for the ones ready to embark on a job search.

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