Advanced Database Management System - Tutorials and Notes: Natural Language Processing (NLP) Multiple Choice Questions with answers 15

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Sunday, 27 September 2020

Natural Language Processing (NLP) Multiple Choice Questions with answers 15

Top 5 MCQ on NLP, NLP quiz questions with answers, NLP MCQ questions, Solved questions in natural language processing, NLP practitioner exam questions


Multiple Choice Questions in NLP

1. In a language, it is usual to have a word with more than one meaning even within its semantic class (polysemy). Which of the following tasks would help us in choose the right meaning as per the context in which the word is used?

(a) Lemmatization

(b) Word Sense Disambiguation

(c) Stemming

(d) Discourse analysis

Answer: (b) Word Sense Disambiguation

Word Sense Disambiguation (WSD)

The ability to computationally determine which sense of a word is activated by its use in a particular context.

 

Polysemy

Polysemy is the capacity for a word or phrase to have multiple meanings, usually related by contiguity of meaning within a semantic field. For example, chicken as a noun can be used in the following senses;

Sense 1: Chicken (noun) – meat of the chicken

Sense 2: Chicken (noun) – a bird

 

2. Which of the following is an advantage of normalizing a word?

(a) It helps in reducing the randomness in the word

(b) It increases the false negatives

(c) It reduces the dimensionality of the input.

(d) All of the above

Answer: (a) and (c)

(a) It helps in reducing the randomness in the word

When we normalize a text using any normalization technique, we actually reduce the word into its base form. A word may be used in different tenses according to the grammar. For example, working, worked, and works are all refer to the same root word ‘work’.  Hence, converting these words into root reduces three different occurrence of a word into one. This helps a lot in NLP.

 

(c) It reduces the dimensionality of the input

As mentioned above, it reduces the number of unique words extracted from a corpus. In this way, it helps in reducing the dimension in machine learning task.

 

3. Which of the following techniques is most appropriate to the process of word normalization?

(a) Lemmatization

(b) Stemming

(c) Stop word removal

(d) Rooting

Answer: (a) Lemmatization

Lemmatization

A word in a language can be inflected into different word to express a grammatical function or attributes. For example,

‘glasses’ is the inflected form of the word ‘glass’ to denote the plural noun.

‘opened, ‘opening’, ‘opens’ are the inflected form of the word ‘open’ to denote the grammatical variation

Lemmatization is the process of removing the inflections of a word in order to map the word to its root form. Example: ‘opened’ to ‘open.

 

Stemming is also does the same work but it uses heuristics to cut down the inflections from a word. This may not always end up in root word. For example, stemming process reduces the word ‘dogs’ to ‘dog’ but reduces the word ‘tried’ to ‘tri’. Hence, it is not most appropriate.

 

4. Words may have multiple meanings. This leads to what type of ambiguity in NLP?

(a) Syntactic ambiguity

(b) Anaphoric ambiguity

(c) Semantic ambiguity

(d) Lexical ambiguity

Answer: (d) Lexical ambiguity

Ambiguity in NLP is the state of being ambiguous usually with more than one interpretation of a word, phrase or sentence.

Lexical ambiguity is a type of ambiguity that occurs when a sentence contains a word that has more than one meaning.

 

5. “I went to the school, and they told me come on next day”. What type of ambiguity present in the given sentence?

(a) Syntactic ambiguity

(b) Anaphoric ambiguity

(c) Semantic ambiguity

(d) Lexical ambiguity

Answer: (b) Anaphoric ambiguity

Anaphoric ambiguity

Anaphoric ambiguity refers to such a situation where an anaphor have more than one possible reference in the same or other sentence. In the given sentence, ‘they’ refers to the school staffs which is actually not present in the given text. [For more information, please refer here]

 

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