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NLP interview questions

Natural Language Processing (NLP) Interview Questions

  • Sharad Jaiswal
  • 05th May, 2021
  • 866 Followers

About NLP

Natural Language Processing ( NLP) is a sub-field of computer science and information engineering that takes the help of artificial intelligence. It deals with how computers deal with human languages. Developers aim at programing the computer to process and analyze natural data in the best way. Natural Language Processing started way back to the 1950s and still a large field for development.

NLP interview questions

1) What is NLP?

Natural Language Processing (NLP) is a branch of AI that helps computers to understand, interpret and manipulate human language.

NLP helps developers to organize and structure knowledge to perform tasks like translation, summarization, named entity recognition, relationship extraction, speech recognition, topic segmentation, etc.

2) List some Components of NLP?

Major components are NLP are

  • Morphological and Lexical Analysis
  • Syntactic Analysis
  • Semantic Analysis
  • Discourse Integration
  • Pragmatic Analysis

3) What are the applications of NLP?

Some popular applications of NLP are

  • Speech Recognition
  • Question Answering
  • Market Intelligence
  • Text Classification
  • Atomatic Summarization
  • Machine Translation
  • Chatbots

4) List some areas of NLP?

NLP is used in many fields, including business, sports, art, health, marketing, education, and politics, in fact, anywhere that involves human endeavor. NLP is widely used in business.

5) What are NLP models?

NLP models are a separate segment which deals with instructed data. The following is a list of some of the most commonly researched tasks in NLP.

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6) What is the significance of TF-IDF?

Tf-idf stands for term frequency-inverse document frequency, and the tf-idf weight is a weight often used in information retrieval and text mining.

7) What is part of speech (POS) tagging?

POS (Part of speech) tagging is the process of marking up a word in a corpus to a corresponding part of a speech tag, based on its context and definition. This task is not straightforward, as a particular word may have a different part of speech based on the context in which the word is used.

8) What is Lemmatization in NLP?

Lemmatization is a form of NLP widely used in Text mining. It is a process of grouping together the inflected forms of a word so they can be analyzed as a single item, identified by the word's lemma, or dictionary form.

9) What is stemming in NLP?

Stemming is the process of reducing a word to its word stem that affixes to suffixes and prefixes or to the roots of words known as a lemma. Stemming is important in natural language understanding (NLU) and natural language processing (NLP).

10) What is tokenization in NLP?

Tokenization in NLP is the process by dividing the quantity of text into smaller parts called tokens. Alternatively, Tokenization is the process of breaking up the given text into units called tokens. The tokens may be words or numbers or punctuation marks. Tokenization does this task by locating word boundaries.

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11) What is chunking in NLP?

Chunking is a linguistic tool used in NLP that allows the speaker to traverse the realms of abstract to specific easily and effortlessly. When we speak or think we use words that indicate how abstract, or how detailed we are in processing the information.

12) What is constituency parsing in NLP?

Constituency parsing aims to extract a constituency-based parse tree from a sentence that represents its syntactic structure according to a phrase structure grammar.

13) Differentiate regular grammar and regular expression.

Regular expression is a sequence of characters that define a search pattern, mainly for use in pattern matching with strings, or string matching. It includes the following elements:

Example: A and B are regular expressions then

  • The regular expression is A. B (concatenation)
  • The regular expression (alternation) is A l B
  • The regular expression (Kleene Star) is A*

Regular Grammars

There are 4 tuples in Regular Grammars (N, ∑, P, S € N). In this formula, N stands for the non-terminals’ sets, ∑ means the set of terminals, P is the set of productions to change the start symbol, P has its productions from one of the types and lastly S is the start non-terminal.

14) List some tools for training NLP models?

TextBlob Library, GenSim, AllenNLP, Stanford Core NLP, Apache OpenNLP are some tools for training NLP models.

15) What are Stopwords in NLTK?

In natural language processing, useless words (data) like 'the', 'is', 'are','me','my','myself','we','our','ours','ourselves','you', etc are referred to as stop words.

16) Explain Named entity recognition (NER)?

Named-entity recognition (NER) is the method of extracting information. It arranges and classifies named entity in the unstructured text in different categories like locations, time expressions, organizations, percentages, and monetary values. NER allows the users to properly understand the subject of the text.

17) What is NLTK?

The Natural Language Toolkit, or more commonly NLTK, is a suite of libraries and programs for symbolic and statistical natural language processing for English written in the Python programming language.

18) What is NLP algorithm?

NLP algorithms are typically based on machine learning algorithms. Instead of hand-coding large sets of rules, NLP can rely on machine learning to automatically learn these rules by analyzing a set of examples (i.e. a large corpus, like a book, down to a collection of sentences), and making a statical inference.

19) What is the difference between NLP and NLU?

Difference between NLP and NLU are

Natural Language Processing Natural Language Understanding
NLP is the system that works simultaneously to manage end-to-end conversations between computers and humans. NLU helps to solve the complicated challenges of Artificial Intelligence. 
NLP is related to both humans and machines. NLU allows converting the unstructured inputs into structured text for easy understanding by the machines.

20) What is the difference between NLP and CI(Conversational Interfaces)?

Difference between NLP and CI(Conversational Interfaces)

Natural Language Processing Conversational Interfaces
NLP is a kind of artificial intelligence technology that allows identifying, understanding and interpreting the request of users in the form of language. CI is a user interface that mixes voice, chat and another natural language with images, videos or buttons. 
NLP aims to make users understand a particular concept. Conversational Interface provides only what the users need and not more than that.

21) List few differences between AI, Machine Learning, and NLP?

Few differences between AI, Machine Learning, and NLP are

Artificial Intelligence Machine Learning Natural Language Processing
It is the technique to create smarter machines Machine Learning is the term used for systems that learn from experience. This is the set of system that has the ability to understand the language
AI includes human intervention Machine Learning purely involves the working of computers and no human intervention. NLP links both computer and human languages.
Artificial intelligence is a broader concept than Machine Learning ML is a narrow concept and is a subset of AI.  

22) Explain the Masked Language Model?

Masked language model is an example of autoencoding language modeling (the output is reconstructed from corrupted input) - we typically mask one or more of words in a sentence and have the model predict those masked words given the other words in sentence.

23) What is latent semantic indexing? Where it is applied.

Latent Semantic Indexing (LSI) also called Latent semantic analysis is a mathematical method that was developed so that the accuracy of retrieving information can be improved. It helps in finding out the hidden(latent) relationship between the words(semantics) by producing a set of various concepts related to the terms of a sentence to improve the information understanding. The technique used for the purpose is called Singular value decomposition.

It is generally useful for working on small sets of static documents.

24) What is pragmatic analysis in NLP?

Pragmatic analysis refers to a set of linguistic and logical tools with which analysts develop systematic accounts of discursive political interactions

25) Explain dependency parsing in NLP?

Dependency parsing is the task of extracting a dependency parse of a sentence that represents its grammatical structure and defines the relationships between "head" words and words, which modify those heads. It is also known as Syntactic Parsing.

26) What is ngram in NLP?

ngram in NLP is a contiguous sequence of n items from a given sample of text or speech. These items can be phonemes, syllables, letters, words or base pairs according to the application. The n-grams typically are collected from a text or speech corpus.

27) What is perplexity in NLP?

In natural language processing, perplexity is a way of evaluating language models. A language model is a probability distribution over entire sentences or texts.

28) What is pragmatic ambiguity in NLP?

Pragmatic ambiguity refers to a situation where the context of a phrase gives it multiple interpretations. One of the hardest tasks in NLP. Pragmatic ambiguity arises when the statement is not specific, and the context does not provide the information needed to clarify the statement

29) What is morphology in NLP?

Morphology is a branch of linguistics that focuses on the way in which words are formed from morphemes. There are two types of morphemes namely lexical morphemes and grammatical morphemes

30) What is smoothing in NLP?

Smoothing is techniques in NLP are used to address scenarios related to determining probability/likelihood estimate of a sequence of words (say, a sentence) occurring together when one or more words individually (unigram) or N-grams such as bigram(w_{i}/w_{i-1}) or trigram (w_{i}/w_{i-1}w_{i-2}) in the given set.

31) What are some open-source NLP libraries?

NLTK, spaCy, AllenNLP, Textacy, PyTorch-NLP, Intel NLP Architect, Gensim and, Flair are some open-source NLP libraries.

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