Showing posts with label NLP Quiz Questions. Show all posts
Showing posts with label NLP Quiz Questions. Show all posts

Wednesday, December 22, 2021

Natural Language Processing MCQ - Lemmatization and Stemming

Multiple choices questions in NLP, Natural Language Processing solved MCQ, What is Lemmatization, What is Stemming, Difference between lemmatization and stemming

NLP MCQ - System crash and state of a transaction

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1. Suppose that there are two techniques A and B. Technique A is used to convert any surface form of a word to its generic or root form. For example ‘students’ and ‘studies’ will be changed to ‘student’ and ‘study’ respectively. Technique B is used to slice a word from some position such that it makes a meaningful word on the basis of how it looks, for example the word ‘caring’ and ‘studies’ will be changed to ‘car’ and ‘studi’ respectively. Which of the following pair of techniques are most appropriate to techniques A and B?

(a) Euclidean distance and Lemmatization

(b) Word tokenization and Stemming

(c) Lemmatization and Stemming

(d) Dependency Parsing and POS Tagging

 

Answer: (c) Lemmatization and Stemming

Lemmatization uses morphological analysis and vocabulary to convert a word from its surface form to root form. Stemming removes the part of a word to find the root word heuristically.

Technique A – Lemmatization. Lemmatization usually refers to finding the root form of words 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.

Technique B – Stemming. Stemming usually refers to a crude heuristic process that chops off the ends of words in the hope of achieving this goal correctly most of the time, and often includes the removal of derivational affixes.

Main difference between lemmatization and stemming;

Lemmatization considers the word and its context along with POS tag to process a word whereas stemming considers neither the context nor the POS category.

More on lemmatization - refer here please



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