Top 59 NLP Interview Questions & Answers 2021

1. What do you understand by Natural Language Processing?

Natural Language Processing is a field of computer science that deals with communication between computer systems and humans. It is a technique used in Artificial Intelligence and Machine Learning. It is used to create automated software that helps understand human spoken languages to extract useful information from the data it gets in the form of audio. Techniques in NLP allow computer systems to process and interpret data in the form of natural languages.

2. List any two real-life applications of Natural Language Processing.

Two real-life applications of Natural Language Processing are as follows:

  1. Google Translate: Google Translate is one of the famous applications of Natural Language Processing. It helps convert written or spoken sentences into any language. Also, we can find the correct pronunciation and meaning of a word by using Google Translate. It uses advanced techniques of Natural Language Processing to achieve success in translating sentences into various languages.
 
  1. Chatbots: To provide a better customer support service, companies have started using chatbots for 24/7 service. Chatbots helps resolve the basic queries of customers. If a chatbot is not able to resolve any query, then it forwards it to the support team, while still engaging the customer. It helps make customers feel that the customer support team is quickly attending them. With the help of chatbots, companies have become capable of building cordial relations with customers. It is only possible with the help of Natural Language Processing.

3. What are stop words?

Stop words are said to be useless data for a search engine. Words such as articles, prepositions, etc. are considered as stop words. There are stop words such as was, were, is, am, the, a, an, how, why, and many more. In Natural Language Processing, we eliminate the stop words to understand and analyze the meaning of a sentence. The removal of stop words is one of the most important tasks for search engines. Engineers design the algorithms of search engines in such a way that they ignore the use of stop words. This helps show the relevant search result for a query.

4. What is NLTK?

NLTK is a Python library, which stands for Natural Language Toolkit. We use NLTK to process data in human spoken languages. NLTK allows us to apply techniques such as parsing, tokenization, lemmatization, stemming, and more to understand natural languages. It helps in categorizing text, parsing linguistic structure, analyzing documents, etc.

A few of the libraries of the NLTK package that we often use in NLP are:

  1. SequentialBackoffTagger
  2. DefaultTagger
  3. UnigramTagger
  4. treebank
  5. wordnet
  6. FreqDist
  7. patterns
  8. RegexpTagger
  9. backoff_tagger
  10. UnigramTagger, BigramTagger, and TrigramTagger

5. What is Syntactic Analysis?

Syntactic analysis is a technique of analyzing sentences to extract meaning from it. Using syntactic analysis, a machine can analyze and understand the order of words arranged in a sentence. NLP employs grammar rules of a language that helps in the syntactic analysis of the combination and order of words in documents.

The techniques used for syntactic analysis are as follows:

  1. Parsing: It helps in deciding the structure of a sentence or text in a document. It helps analyze the words in the text based on the grammar of the language.
  2. Word segmentation: The segmentation of words segregates the text into small significant units.
  3. Morphological segmentation: The purpose of morphological segmentation is to break words into their base form.
  4. Stemming: It is the process of removing the suffix from a word to obtain its root word.
  5. Lemmatization: It helps combine words using suffixes, without altering the meaning of the word.

6. What is Semantic Analysis?

Semantic analysis helps make a machine understand the meaning of a text. It uses various algorithms for the interpretation of words in sentences. It also helps understand the structure of a sentence.

Techniques used for semantic analysis are as given below:

  1. Named entity recognition: This is the process of information retrieval that helps identify entities such as the name of a person, organization, place, time, emotion, etc.
  2. Word sense disambiguation: It helps identify the sense of a word used in different sentences.
  3. Natural language generation: It is a process used by the software to convert the structured data into human spoken languages. By using NLG, organizations can automate content for custom reports.

7. List the components of Natural Language Processing.

The major components of NLP are as follows:

  • Entity extraction: Entity extraction refers to the retrieval of information such as place, person, organization, etc. by the segmentation of a sentence. It helps in the recognition of an entity in a text.
  • Syntactic analysis: Syntactic analysis helps draw the specific meaning of a text.
  • Pragmatic analysis: To find useful information from a text, we implement pragmatic analysis techniques.
  • Morphological and lexical analysis: It helps in explaining the structure of words by analyzing them through parsing.

8. What is Latent Semantic Indexing (LSI)?

Latent semantic indexing is a mathematical technique used to improve the accuracy of the information retrieval process. The design of LSI algorithms allows machines to detect the hidden (latent) correlation between semantics (words). To enhance information understanding, machines generate various concepts that associate with the words of a sentence.

The technique used for information understanding is called singular value decomposition. It is generally used to handle static and unstructured data. The matrix obtained for singular value decomposition contains rows for words and columns for documents. This method best suits to identify components and group them according to their types.

The main principle behind LSI is that words carry a similar meaning when used in a similar context. Computational LSI models are slow in comparison to other models. However, they are good at contextual awareness that helps improve the analysis and understanding of a text or a document.

9. What are Regular Expressions?

A regular expression is used to match and tag words. It consists of a series of characters for matching strings.

Suppose, if A and B are regular expressions, then the following are true for them:

  • If {ɛ} is a regular language, then ɛ is a regular expression for it.
  • If A and B are regular expressions, then A + B is also a regular expression within the language {A, B}.
  • If A and B are regular expressions, then the concatenation of A and B (A.B) is a regular expression.
  • If A is a regular expression, then A* (A occurring multiple times) is also a regular expression.

10. What is Regular Grammar?

Regular grammar is used to represent a regular language.

A regular grammar comprises rules in the form of A -> aA -> aB, and many more. The rules help detect and analyze strings by automated computation.

Regular grammar consists of four tuples:

  1. ‘N’ is used to represent the non-terminal set.
  2. ‘∑’ represents the set of terminals.
  3. ‘P’ stands for the set of productions.
  4. ‘S € N’ denotes the start of non-terminal.

11. What is Parsing in the context of NLP?

Parsing in NLP refers to the understanding of a sentence and its grammatical structure by a machine. Parsing allows the machine to understand the meaning of a word in a sentence and the grouping of words, phrases, nouns, subjects, and objects in a sentence. Parsing helps analyze the text or the document to extract useful insights from it. To understand parsing, refer to the below diagram:

In this, ‘Jonas ate an orange’ is parsed to understand the structure of the sentence.

Intermediate NLP Interview Questions

12. What is TF-IDF?

TFIDF or Term Frequency-Inverse Document Frequency indicates the importance of a word in a set. It helps in information retrieval with numerical statistics. For a specific document, TF-IDF shows a frequency that helps identify the keywords in a document. The major use of TF-IDF in NLP is the extraction of useful information from crucial documents by statistical data. It is ideally used to classify and summarize the text in documents and filter out stop words.

TF helps calculate the ratio of the frequency of a term in a document and the total number of terms. Whereas, IDF denotes the importance of the term in a document. The formula for calculating TF-IDF: TF(W) = (Frequency of W in a document)/(The total number of terms in the document) IDF(W) = log_e(The total number of documents/The number of documents having the term W) When TF*IDF is high, the frequency of the term is less and vice versa. Google uses TF-IDF to decide the index of search results according to the relevancy of pages. The design of the TF-IDF algorithm helps optimize the search results in Google. It helps quality content rank up in search results. If you want to know more about ‘What is Natural Language Processing?’ you can go through this Natural Language Processing Using Python course!

13. Define the terminology in NLP.

This is one of the most often asked NLP interview questions.

The interpretation of Natural Language Processing depends on various factors, and they are:

 

Weights and Vectors

  • Use of TF-IDF for information retrieval
  • Length (TF-IDF and doc)
  • Google Word Vectors
  • Word Vectors
Structure of the Text

  • POS tagging
  • Head of the sentence
  • Named Entity Recognition (NER)

Sentiment Analysis

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

Classification of Text

  • Supervised learning algorithm
  • Training set
  • Validation set
  • Test set
  • Features of the text
  • LDA

Machine Reading

  • Removal of possible entities
  • Joining with other entities
  • DBpedia

14. Explain Dependency Parsing in NLP.

Dependency parsing helps assign a syntactic structure to a sentence. Therefore, it is also called syntactic parsing. Dependency parsing is one of the critical tasks in NLP. It allows the analysis of a sentence using parsing algorithms. Also, by using the parse tree in dependency parsing, we can check the grammar and analyze the semantic structure of a sentence.

For implementing dependency parsing, we use the spacy package. It implements token properties to operate the dependency parse tree.

The below diagram shows the dependency parse tree:

15. What is the difference between NLP and NLU?

The below table shows the difference between NLP and NLU:

16. What is the difference between NLP and CI?

The below table shows the difference between NLP and CI:

 

17. What is Pragmatic Analysis?

Pragmatic analysis is an important task in NLP for interpreting knowledge that is lying outside a given document. The aim of implementing pragmatic analysis is to focus on exploring a different aspect of the document or text in a language. This requires a comprehensive knowledge of the real world. The pragmatic analysis allows software applications for the critical interpretation of the real-world data to know the actual meaning of sentences and words.

Example:

Consider this sentence: ‘Do you know what time it is?’

This sentence can either be asked for knowing the time or for yelling at someone to make them note the time. This depends on the context in which we use the sentence.

18. What is Pragmatic Ambiguity?

Pragmatic ambiguity refers to the multiple descriptions of a word or a sentence. An ambiguity arises when the meaning of the sentence is not clear. The words of the sentence may have different meanings. Therefore, in practical situations, it becomes a challenging task for a machine to understand the meaning of a sentence. This leads to pragmatic ambiguity.

Example:

Check out the below sentence.

‘Are you feeling hungry?’

The given sentence could be either a question or a formal way of offering food.

19. What are unigrams, bigrams, trigrams, and n-grams in NLP?

When we parse a sentence one word at a time, then it is called a unigram. The sentence parsed two words at a time is a bigram.

When the sentence is parsed three words at a time, then it is a trigram. Similarly, n-gram refers to the parsing of n words at a time.

Example: To understand unigrams, bigrams, and trigrams, you can refer to the below diagram:

 

Therefore, parsing allows machines to understand the individual meaning of a word in a sentence. Also, this type of parsing helps predict the next word and correct spelling errors.

20. What are the steps involved in solving an NLP problem?

Below are the steps involved in solving an NLP problem:

  1. Gather the text from the available dataset or by web scraping
  2. Apply stemming and lemmatization for text cleaning
  3. Apply feature engineering techniques
  4. Embed using word2vec
  5. Train the built model using neural networks or other Machine Learning techniques
  6. Evaluate the model’s performance
  7. Make appropriate changes in the model
  8. Deploy the model

21. What is Feature Extraction in NLP?

Features or characteristics of a word help in text or document analysis. They also help in sentiment analysis of a text. Feature extraction is one of the techniques that are used by recommendation systems. Reviews such as ‘excellent,’ ‘good,’ or ‘great’ for a movie are positive reviews, recognized by a recommender system. The recommender system also tries to identify the features of the text that help in describing the context of a word or a sentence. Then, it makes a group or category of the words that have some common characteristics. Now, whenever a new word arrives, the system categorizes it as per the labels of such groups.

22. What is precision and recall?

The metrics used to test an NLP model are precision, recall, and F1. Also, we use accuracy for evaluating the model’s performance. The ratio of prediction and the desired output yields the accuracy of the model.

Precision is the ratio of true positive instances and the total number of positively predicted instances.

Recall is the ratio of true positive instances and the total actual positive instances.

23. What is F1 score in NLP?

F1 score evaluates the weighted average of recall and precision. It considers both false negative and false positive instances while evaluating the model. F1 score is more accountable than accuracy for an NLP model when there is an uneven distribution of class. Let us look at the formula for calculating F1 score:

Advanced NLP Interview Questions

24. How to tokenize a sentence using the nltk package?

Tokenization is a process used in NLP to split a sentence into tokens. Sentence tokenization refers to splitting a text or paragraph into sentences.

For tokenizing, we will import sent_tokenize from the nltk package:

from nltk.tokenize import sent_tokenize<>

We will use the below paragraph for sentence tokenization:

Para = “Hi Guys. Welcome to Deepneuron. This is a blog on the NLP interview questions and answers.”

  sent_tokenize(Para)

Output:

  [ 'Hi Guys.' ,
  'Welcome to Deepneuron. ',
  'This is a blog on the NLP interview questions and answers. ' ] 

Tokenizing a word refers to splitting a sentence into words.

Now, to tokenize a word, we will import word_tokenize from the nltk package.

  from nltk.tokenize import word_tokenize

Para = “Hi Guys. Welcome to Deepneuron. This is a blog on the NLP interview questions and answers.”

  word_tokenize(Para)

Output:

  [ 'Hi' , 'Guys' , ' . ' , 'Welcome' , 'to' , 'Deepneuron' , ' . ' , 'This' , 'is' ,   'a', 'blog' , 'on' , 'the' , 'NLP' , 'interview' , 'questions' , 'and' , 'answers' , ' . ' ]

25. Explain how we can do parsing.

Parsing is the method to identify and understand the syntactic structure of a text. It is done by analyzing the individual elements of the text. The machine parses the text one word at a time, then two at a time, further three, and so on.

  • When the machine parses the text one word at a time, then it is a unigram.
  • When the text is parsed two words at a time, it is a bigram.
  • The set of words is a trigram when the machine parses three words at a time.

Look at the below diagram to understand unigram, bigram, and trigram.

 

Now, let’s implement parsing with the help of the nltk package.

  import nltk
  text = ”Top 30 NLP interview questions and answers”

We will now tokenize the text using word_tokenize.

  text_token= word_tokenize(text)

Now, we will use the function for extracting unigrams, bigrams, and trigrams.

  list(nltk.unigrams(text))

Output:

  [ "Top 30 NLP interview questions and answer"]
  list(nltk.bigrams(text))

Output:

  ["Top 30", "30 NLP", "NLP interview", "interview questions",   "questions and", "and answer"]
  list(nltk.trigrams(text))

Output:

  ["Top 30 NLP", "NLP interview questions", "questions and answers"]

For extracting n-grams, we can use the function nltk.ngrams and give the argument n for the number of parsers.

  list(nltk.ngrams(text,n))

26. Explain Stemming with the help of an example.

In Natural Language Processing, stemming is the method to extract the root word by removing suffixes and prefixes from a word. For example, we can reduce ‘stemming’ to ‘stem’ by removing ‘m’ and ‘ing.’ We use various algorithms for implementing stemming, and one of them is PorterStemmer. First, we will import PorterStemmer from the nltk package.

  from nltk.stem import PorterStemmer

Creating an object for PorterStemmer

  pst=PorterStemmer()
  pst.stem(“running”), pst.stem(“cookies”), pst.stem(“flying”)

Output:

  (‘run’, ‘cooki', ‘fly’ )

27. Explain Lemmatization with the help of an example.

We use stemming and lemmatization to extract root words. However, stemming may not give the actual word, whereas lemmatization generates a meaningful word. In lemmatization, rather than just removing the suffix and the prefix, the process tries to find out the root word with its proper meaning. Example: ‘Bricks’ becomes ‘brick,’ ‘corpora’ becomes ‘corpus,’ etc. Let’s implement lemmatization with the help of some nltk packages. First, we will import the required packages.

  from nltk.stem import wordnet
  from nltk.stem import WordnetLemmatizer

Creating an object for WordnetLemmatizer()

  lemma= WordnetLemmatizer()
  list = [“Dogs”, “Corpora”, “Studies”]
  for n in list:
  print(n + “:” + lemma.lemmatize(n))

Output:

  Dogs: Dog
  Corpora: Corpus
  Studies: Study

28. What is Parts-of-speech Tagging?

The parts-of-speech (POS) tagging is used to assign tags to words such as nouns, adjectives, verbs, and more. The software uses the POS tagging to first read the text and then differentiate the words by tagging. The software uses algorithms for the parts-of-speech tagging. POS tagging is one of the most essential tools in Natural Language Processing. It helps in making the machine understand the meaning of a sentence. We will look at the implementation of the POS tagging using stop words. Let’s import the required nltk packages.

  import nltk
  from nltk.corpus import stopwords
  from nltk.tokenize import word_tokenize, sent_tokenize
  stop_words = set(stopwords.words('english'))
  txt = "Sourav, Pratyush, and Abhinav are good friends."

Tokenizing using sent_tokenize

  tokenized_text = sent_tokenize(txt)

To find punctuation and words in a string, we will use word_tokenizer and then remove the stop words.

  for n in tokenized_text:
  wordsList = nltk.word_tokenize(i)
  wordsList = [w for w in wordsList if not w instop_words]

Now, we will use the POS tagger.

  tagged_words = nltk.pos_tag(wordsList)
  print(tagged_words)

Output:

  [('Sourav', 'NNP'), ('Pratyush', 'NNP'), ('Abhinav', 'NNP'), ('good',  'JJ'), ('friends', 'NNS')]

29. Explain Named Entity Recognition by implementing it.

Named Entity Recognition (NER) is an information retrieval process. NER helps classify named entities such as monetary figures, location, things, people, time, and more. It allows the software to analyze and understand the meaning of the text. NER is mostly used in NLP, Artificial Intelligence, and Machine Learning. One of the real-life applications of NER is chatbots used for customer support.

Let’s implement NER using the spacy package.

Importing the spacy package:

  import spacy
  nlp = spacy.load('en_core_web_sm')
  Text = "The head office of Google is in California"
  document = nlp(text)for ent in document.ents:
  print(ent.text, ent.start_char, ent.end_char, ent.label_)

Output:

  Office 9 15 Place
  Google 19 25 ORG
  California 32 41 GPE
 

30. How to check word similarity using the spacy package?

To find out the similarity among words, we use word similarity. We evaluate the similarity with the help of a number that lies between 0 and 1. We use the spacy library to implement the technique of word similarity.

  import spacy
  nlp = spacy.load('en_core_web_md')
  print("Enter the words")
  input_words = input()
  tokens = nlp(input_words)
  for i in tokens:
  print(i.text, i.has_vector, i.vector_norm, i.is_oov)
  token_1, token_2 = tokens[0], tokens[1]
  print("Similarity between words:", token_1.similarity(token_2))

Output:

  hot  True 5.6898586 False
  cold True6.5396233 False
  Similarity: 0.597265

This means that the similarity between the words ‘hot’ and ‘cold’ is just 59 percent.

31) What is NLP?

Natural Language Processing or NLP is an automated way to understand or analyze the natural languages and extract required information from such data by applying machine learning Algorithms.

32) List some Components of NLP?

Below are the few major components of NLP.

  • Entity extraction: It involves segmenting a sentence to identify and extract entities, such as a person (real or fictional), organization, geographies, events, etc.
  • Syntactic analysis: It refers to the proper ordering of words.
  • Pragmatic analysis: Pragmatic Analysis is part of the process of extracting information from text.

33) List some areas of NLP?

Natural Language Processing can be used for

  • Semantic Analysis
  • Automatic summarization
  • Text classification
  • Question Answering

Some real-life example of NLP is IOS Siri, the Google assistantAmazon echo.

34) Define the NLP Terminology?

NLP Terminology is based on the following factors:

  • Weights and Vectors: TF-IDF, length(TF-IDF, doc), Word Vectors, Google Word Vectors
  • Text Structure: Part-Of-Speech Tagging, Head of sentence, Named entities
  • Sentiment Analysis: Sentiment Dictionary, Sentiment Entities, Sentiment Features
  • Text Classification: Supervised Learning, Train Set, Dev(=Validation) Set, Test Set, Text Features, LDA.
  • Machine Reading: Entity Extraction, Entity Linking,dbpedia, FRED (lib) / Pikes

35) What is the significance of TF-IDF?

tf–idf or TFIDF stands for term frequency–inverse document frequency. In information retrieval TFIDF is is a numerical statistic that is intended to reflect how important a word is to a document in a collection or in the collection of a set.

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

According to The Stanford Natural Language Processing Group :

Part-Of-Speech Tagger (POS Tagger) is a piece of software that reads text in some language and assigns parts of speech to each word (and other token), such as noun, verb, adjective, etc.

PoS taggers use an algorithm to label terms in text bodies. These taggers make more complex categories than those defined as basic PoS, with tags such as “noun-plural” or even more complex labels. Part-of-speech categorization is taught to school-age children in English grammar, where children perform basic PoS tagging as part of their education.

37) What is Lemmatization in NLP?

Lemmatization generally means to do the things properly with the use of vocabulary and morphological analysis of words. In this process, the endings of the words are removed to return the base word, which is also known as Lemma.

Example: boy’s = boy, cars= car, colors= color.

So, the main attempt of Lemmatization as well as of stemming is to identify and return the root words of the sentence to explore various additional information.

38) 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). ... Stemming is also a part of queries and Internet search engines.

39) What is tokenization in NLP?

Natural Language Processing aims to program computers to process large amounts of natural language data. Tokenization in NLP means the method of dividing the text into various tokens. You can think of a token in the form of the word. Just like a word forms into a sentence. It is an important step in NLP to slit the text into minimal units.

40) What is latent semantic indexing and where can it be 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.

41) What is dependency parsing?

      Dependency parsing is the process of analyzing the grammatical structure of a sentence based on the dependencies between the words in a sentence. In Dependency parsing, various tags represent the relationship between two words in a sentence.

42) 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.

43) List some tools for training NLP models?

  • MonkeyLearn. MonkeyLearn is a user-friendly, NLP-powered platform that helps you gain valuable insights from your text data. ...
  • Aylien. ...
  • IBM Watson. ...
  • Google Cloud. ...
  • Amazon Comprehend. ...
  • NLTK. ...
  • Stanford Core NLP. ...
  • TextBlob.

44) Describe dependency parsing?

     

 A dependency parser analyzes the grammatical structure of a sentence, establishing relationships between "head" words and words which modify those heads. The figure below shows a dependency parse of a short sentence. ... A Fast and Accurate Dependency Parser Using Neural Networks.

45) 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.

46) 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.

47) List some OpenSource Libraries for NLP?

  • NLTK.
  • Stanford Core NLP.
  • Apache OpenNLP.
  • SpaCy.
  • AllenNLP.
  • GenSim.
  • TextBlob Library.
  • Intel NLP Architect.

48) 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.

49) 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.

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

Differences between AI, Machine Learning, and NLP

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.

51) Explain the Masked Language Model?

Masked language modelling is the process in which the output is taken from the corrupted input. This model helps the learners to master the deep representations in downstream tasks. You can predict a word from the other words of the sentence using this model.

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

     Latent semantic indexing (LSI) is a concept used by search engines to discover how a term and content work together to mean the same thing, even if they do not share keywords or synonyms. This layman's explanation of LSI will discuss everything about LSI and how to take advantage of it for your business.

53) What is pragmatic analysis in NLP?

Pragmatic Analysis: It deals with outside word knowledge, which means knowledge that is external to the documents and/or queries. Pragmatics analysis that focuses on what was described is reinterpreted by what it actually meant, deriving the various aspects of language that require real-world knowledge.

54) Explain dependency parsing in NLP?

Dependency Parsing is also known as Syntactic Parsing. It is the task of recognizing a sentence and assigning a syntactic structure to it. The most widely used syntactic structure is the parse tree which can be generated using some parsing algorithms. These parse trees are useful in various applications like grammar checking or more importantly it plays a critical role in the semantic analysis stage.

55) What is pragmatic ambiguity in NLP?

Pragmatic Ambiguity can be defined as the words which have multiple interpretations. Pragmatic Ambiguity arises when the meaning of words of a sentence is not specific; it concludes different meanings. There are various sentences in which the proper sense is not understood due to the grammar formation of the sentence; this multi interpretation of the sentence gives rise to ambiguity. For example- "do you want a cup of coffee", the given the word is either an informative question or a formal offer to make a cup coffee.

56) What is perplexity in NLP?

The word "perplexed" means "puzzled" or "confused", thus Perplexity in general means the inability to tackle something complicated and a problem that is not specified. Therefore, Perplexity in NLP is a way to determine the extent of uncertainty in predicting some text. In NLP, perplexity is a way of evaluating language models. Perplexity can be high and low; Low perplexity is ethical because the inability to deal with any complicated problem is less while high perplexity is terrible because the failure to deal with a complicated is high.

57) What is ngram in NLP?

N-gram in NLP is simply a sequence of n words, and we also conclude the sentences which appeared more frequently, for example, let us consider the progression of these three words:

  • New York (2 gram)
  • The Golden Compass (3 gram)
  • She was there in the hotel (4 gram)

Now from the above sequence, we can easily conclude that sentence (a) appeared more frequently than the other two sentences, and the last sentence(c) is not seen that often. Now if we assign probability in the occurrence of an n-gram, then it will be advantageous. It would help in making next-word predictions and in spelling error corrections.

58) What is Meta Model in NLP?

The meta-model in neuro-linguistic programming is a set of questions designed to specify information, challenge and expand the limits to a person's model of the world. It responds to the distortions, generalizations, and deletions in the speaker's language.

59) Please explain Milton Model?

NLP Milton Model is a set of language patterns used to help people to make desirable changes and solve difficult problems. It is also useful for inducing trance or an altered state of consciousness to access our all powerful unconscious resources.

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