Natural Language Processing (NLP) MCQ's

Question 152 :
I appoint you chairman of the committee is which type of speech act?

1. Commissives
2. Directives
3. Declarations
4. Representatives

Question 153 :
In Probability Ranking Principal, Ranking documents in order of ____________ probability of relevance is optimal.

1. Increasing
2. Decreasing
3. Anyway
4. Steady

Question 154 :
Which type of semantics is concerned with the linguistic study of systematic, meaning related structure of words or lexemes

1. Compund Semantics
2. Lexical semantics
3. Compositional semantics
4. Word Semantics

Question 155 :
e.g. Original statement in speech is 'I saw a van' During speech to text conversion statement becomes eye awe of an Such type of error can be removed by

1. Parser
2. Tagger
3. N-gram
4. FST

Question 156 :
How many uni-grams phrases can be generated from the following sentence, after performing following text cleaning steps: Stop word Removal and Replacing punctuations by a single space i. 'Delhi is the capital of but Mumbai is the financial capital of India.'

1. 8
2. 7
3. 6
4. 5

Question 157 :
________________ used to point to things (it, this, these) and people (him, them, those idiots).

1. Spatial deixis
2. Pragmatics
3. Temporal deixis
4. Personal deixis

Question 158 :
What is the role of NLP in recommendation engines like Collaborative Filtering?

1. Extracting features from text
2. Measuring semantic similarity
3. Constructing feature vector
4. All of the mentioned

Question 159 :
In the sentence, 'They bought a blue house', the underlined part is an example of _____.

1. Noun phrase
2. Verb phrase
3. Prepositional phrase
4. Adverbial phrase

Question 160 :
The steps of preprocessing in Natural Language Processing does not include..

1. Stemming
2. Tokenization
3. Stop Word Removal
4. Segmantation

Question 161 :
Get (to take) - get (to become), is example of ______

1. Synonym
2. Hyponym
3. Homonym
4. Polysemy

Question 162 :
two or more words with the same form and related meanings by extension (foot of a person, of a bed, of a mountain); based on similarity

1. Metonymy
2. Hyponymy
3. Polysemy
4. Hyponym

Question 163 :
Syntax Analyser is also known as __________________.

1. Hierarchical Analysis
2. Sequential Analysis
3. General Analysis
4. Hierarchical Analysis and Parsing

Question 164 :
Is Inflectional morphology performed in google translation?

1. Performed
2. Not performed
3. Partly performed
4. Depends on situation

Question 165 :
Which Application Of Nlp Deals With Creation Of Summaries Of Documents

1. Text Summarization
2. Question Answering
3. Information Extraction
4. Information Retrieval

Question 166 :
Parts-of-Speech tagging determines ___________ . 1) part-of-speech for each word dynamically as per meaning of the sentence 2) part-of-speech for each word dynamically as per sentence structure 3) all part-of-speech for a specific word given as input

1. Only 1 is correct
2. 1 and 2 are correct
3. 1 and 3 are correct
4. All (1,2 and 3) are correct.

Question 167 :
Which of the following entities are identified by NER?

1. Proper Nouns
2. Noun Phrase
3. Verb Phrase
4. Adverb

Question 168 :
Named Entity Recognition means:

1. Finding spans of text that constitute proper names and then classifying the type of the entity.
2. Mapping between name and entity.
3. Classification of text into subject and predicates.
4. Searching text for proper nouns.

Question 169 :
Bat is flying in the sky' Identify the dependency checking to perform sense disambiguation of ‘Bat’

1. Batà sky
2. Skyà fly
3. Batà fly
4. Batà sky, fly

Question 170 :
What is 'indefinite noun phrases' in reference phonomena?

1. Introduces entities that are new to the hearer into the discourse context
2. Introduces entities that are previous or old to the hearer into the discourse context
3. Entities that accept the irregular pharses
4. Entities that accept the regular pharses

Question 171 :
Which NLP application involves conversion of Hindi text into SQL queries

1. Natural Language Convertion to Database
2. Information retrieval
3. Natural Language Extraction from Database
4. Natural Language Interface to Database

Question 172 :
Maximum Entropy Markov Models use a maximum entropy _______for _______ and local __________.

1. Framework, Features, Normalization
2. Rules, Variables, Classification
3. Sets, Values, Distribution
4. Rules, features, classification

Question 173 :
Visiting relatives can be boring

1. The text is unambiguous
2. The text is ambiguous
3. The text clear and precise
4. The text is indisputable

Question 174 :
Yesterday I went to college contains __________type of deixis.

1. Personal
2. Time
3. Social
4. Space

Question 175 :
Semantic model is not used for

1. The meaning of words
2. Knowledge about structure of discourse
3. Common sense knowledge about the topic
4. POS tag of word

Question 176 :
A verb phrase cannot have a

1. a verb followed by an NP {VP → Verb NP}
2. a verb followed by a PP {VP → Verb PP}
3. a verb followed by two NPs {VP → Verb NP NP}
4. a verb followed by two APs {VP → Verb AP AP}

Question 177 :
______________ deals with analyzing emotions, feelings and attitude of speaker or writer from given piece of text

1. Semantic Analysis
2. Sentiment Analysis
3. Information Retrival
4. Text classification

Question 178 :
Which of the following is not a primitive operation of a regular expression?

1. Concatenation
2. Closure
3. Union
4. Projection

Question 179 :
Parts-of-Speech tagging determines ___________

1. part-of-speech for each word dynamically as per meaning of the sentence
2. part-of-speech for each word dynamically as per sentence structure
3. all part-of-speech for a specific word given as input
4. every thing mentioned above

Question 180 :
The statement 'Which mobiles can you show me in your shop?' can be represented as

1. N->Wh-NP Aux NP VP
2. S->Wh-NP Aux NP NP
3. S->Wh-VP Aux NP VP
4. S->Wh-NP Aux NP VP