Natural Language Processing Using Python Interview Questions

Checkout Vskills Interview questions with answers in Natural Language Processing using Python to prepare for your next job role. The questions are submitted by professionals to help you to prepare for the Interview.

Q.1 Why Python is used in natural language processing?
Object-oriented − Python is object-oriented in nature and it makes this language easier to write programs because with the help of this technique of programming it encapsulates code within objects.
Q.2 Which NLP model gives the best accuracy?
Naive Bayes is the most precise model, with a precision of 88.35%, whereas Decision Trees have a precision of 66%.
Q.3 What is Tokenization in NLP?
Tokenization is essentially splitting a phrase, sentence, paragraph, or an entire text document into smaller units, such as individual words or terms. Each of these smaller units are called tokens.
Q.4 Is NLP a classification problem?
NLP system needs to understand text, sign, and semantic properly. They are text classification, vector semantic, word embedding, probabilistic language model, sequence labelling, and speech reorganization.
Q.5 What are stop words?
Stopwords are the English words which does not add much meaning to a sentence. They can safely be ignored without sacrificing the meaning of the sentence. For example, the words like the, he, have etc. Such words are already captured this in corpus named corpus.
Q.6 What is text mining in Python?
Text Mining is the process of deriving meaningful information from natural language text.
Q.7 What is clustering in NLP?
Clustering is a process of grouping similar items together. Each group, also called as a cluster, contains items that are similar to each other. Clustering algorithms are unsupervised learning algorithms i.e. we do not need to have labelled datasets.
Q.8 What is summarization in NLP?
Text summarization in NLP is the process of summarizing the information in large texts for quicker consumption.
Q.9 What is NLTK library in Python?
NLTK is a standard python library that provides a set of diverse algorithms for NLP. It is one of the most used libraries for NLP and Computational Linguistics.
Q.10 What is corpus in Python?
Corpora is a group presenting multiple collections of text documents. A single collection is called corpus. One such famous corpus is the Gutenberg Corpus which contains some 25,000 free electronic books, hosted at http://www.gutenberg.org/.
Q.11 What is flat clustering?
Flat clustering creates a flat set of clusters without any explicit structure that would relate clusters to each other. Hierarchical clustering creates a hierarchy of clusters.
Q.12 Differentiate between stemming and Lemmatization?
Stemming and Lemmatization both generate the foundation sort of the inflected words and therefore the only difference is that stem may not be an actual word whereas, lemma is an actual language word. Stemming follows an algorithm with steps to perform on the words which makes it faster.
Q.13 What is NLTK WordNet?
WordNet is a lexical database for the English language, which was created by Princeton, and is part of the NLTK corpus. You can use WordNet alongside the NLTK module to find the meanings of words, synonyms, antonyms, and more.
Q.14 What is Natural Language Processing?
Natural language processing (NLP) refers to the branch of artificial intelligence or AI—concerned with giving computers the ability to understand text and spoken words in much the same way human beings can.
Q.15 What do you understand by TF-IDF?
TF-IDF stands for “Term Frequency — Inverse Document Frequency”. This is a technique to quantify words in a set of documents. We generally compute a score for each word to signify its importance in the document and corpus.
Q.16 What do you understand by Syntactic Analysis?
Syntactic analysis, also referred to as syntax analysis or parsing, is the process of analyzing natural language with the rules of a formal grammar. Grammatical rules are applied to categories and groups of words, not individual words.
Q.17 What do you understand by Semantic Analysis?
Semantic analysis is the task of ensuring that the declarations and statements of a program are semantically correct, i.e., that their meaning is clear and consistent with the way in which control structures and data types are supposed to be used.
Q.18 What is NLTK?
The Natural Language Toolkit, or NLTK, is a suite of libraries and programs for symbolic and statistical natural language processing for English written in the Python programming language
Q.19 What do you understand by parsing in NLP?
parsing in NLP is the process of determining the syntactic structure of a text by analyzing its constituent words based on an underlying grammar (of the language).
Q.20 What do you understand by 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).
Q.21 Why is stemming done in NLP?
Thus, although a word may exist in several inflected forms, having multiple inflected forms inside the same text adds redundancy to the NLP process. As a result, we employ stemming to reduce words to their basic form or stem, which may or may not be a legitimate word in the language.
Q.22 What are the types of stemming algorithms?
Stemming algorithms can be classified in three groups: truncating methods, statistical methods, and mixed methods. Each of these groups has a typical way of finding the stems of the word variants.
Q.23 How is stemming useful in text summarization?
In Automatic Text Summarization, pre-processing is an important phase to reduce the space of textual representation. Classically, stemming and lemmatization have been widely used for normalizing words.
Q.24 Is stemming beneficial to improving performance?
A stemming is a technique used to reduce words to their root form, by removing derivational and inflectional affixes. Stemming improves the performance of information retrieval systems.
Q.25 What is Lemmatizer in NLP?
Lemmatization usually refers to doing things properly with the use of a vocabulary and morphological analysis of words, normally aiming to remove inflectional endings only and to return the base or dictionary form of a word, which is known as the lemma.
Q.26 Why is stemming important?
When a form of a word is recognized it can make it possible to return search results that otherwise might have been missed. That additional information retrieved is why stemming is integral to search queries and information retrieval. When a new word is found, it can present new research opportunities.
Q.27 When should you go with stemming and lemmatization?
Go with stemming when the vocab space is small and the documents are large. Conversely, go with word embeddings when the vocab space is large but the documents are small. However, don't use lemmatization as the increased performance to increased cost ratio is quite low.
Q.28 What is POS tagging in NLP?
Part-of-speech (POS) tagging is a popular Natural Language Processing process which refers to categorizing words in a text (corpus) in correspondence with a particular part of speech, depending on the definition of the word and its context.
Q.29 What languages are supported by NLTK?
Languages supported by NLTK depends on the task being implemented. For stemming, we have RSLPStemmer (Portuguese), ISRIStemmer (Arabic), and SnowballStemmer (Danish, Dutch, English, Finnish, French, German, Hungarian, Italian, Norwegian, Portuguese, Romanian, Russian, Spanish, Swedish).
Q.30 Why do you want to work as NLP professional at this company?
Working as NLP professional at this company offers me more many avenues of growth and enhance my NLP skills. Your company has been in the domain of linguistics related research and hence offers opportunities for future growth in NLP role. Also considering my education, skills and experience I see myself, more apt for the post.
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