WorksheetsNatural Language Processing Quiz
Total questions: 50
Worksheet time: 38mins
Which of the following is true about natural language?
It is always logically structured
It is machine-optimized
It follows strict grammar rules only
It is rich, flexible, and ambiguous
WhatsApp chats and voice commands are examples of
Programming language
Code language
Structured language
Natural language
NLP is a part of
Hardware engineering
Cloud architecture
Classical linguistics
Artificial intelligence
The main goal of NLP is to
Create human-like robots
Improve database speed
Replace human speech
Enable computer-human language interaction
NLP helps computers
Avoid user inputs
Run faster code
Make ethical decisions
Understand, interpret and generate human language
NLU focuses on
Data storage
Speech output
Translation
Understanding the meaning of text
NLG stands for
Neural Language Grid
Named Lexical Grammar
Numeric Logic Guide
Natural Language Generation
Syntax in NLP is concerned with
Speaker emotions
Context of interaction
Background noise
Arrangement of words in sentences
Semantics helps NLP systems
Spell check accurately
Improve UI design
Match speech tone
Understand the meaning of language
Pragmatics in NLP deals with
Grammar rules
Sound wave filtering
Syntax errors
Speaker’s intent and context
The relationship between sentences is analyzed using
Semantics
Syntax
Pragmatics
Discourse
The NLP component responsible for outputting text is
Parsing engine
Acoustic model
Tokenizer
Natural Language Generation (NLG)
The first step in NLP software is usually
Encoding data
Filtering emails
Synthesizing speech
Breaking down language into parts
NLP tools convert text into machine-understandable formats using
Bitmap encoding
Syntax trees
Rule-based mapping
Text vectorization
Which of the following improves NLP model accuracy?
Adding stop words
Limiting vocabulary
Reducing data input
Feeding more training data
Which is an application of NLP?
Image segmentation
Load balancing
Data encryption
Chatbots
Sentiment analysis is also called
Syntax tagging
Intent labelling
Frequency analysis
Opinion mining
NLP is used in spam detection to
Compress emails
Improve UI
Translate messages
Filter unwanted messages
Text summarization in NLP aims to
Format documents
Increase font size
Replace paragraphs
Shorten content while preserving meaning
Speech recognition converts
Videos to gifs
Images to text
Sound to binary
Spoken words to text
NLP-based machine translation converts
Java to Python
Voice to music
PDFs to audio
One natural language to another
In NLP, spelling correction is commonly seen in
Antivirus software
Video editors
Graphic design tools
Word processors
In speech recognition, a decoder is used to
Clean microphones
Adjust speaker pitch
Control volume
Generate final recognized text
Chatbots use NLP to
Build charts
Open system files
Perform calculations
Simulate conversation
A phoneme is
A speech app
A syntax error
An audio clip
The smallest sound unit that affects meaning
Acoustic signals in speech processing are
Stored as videos
Constant text streams
Repeating loops
Continuous waveforms of sound
Spoken Language Processing is primarily focused on
Language translation
Machine vision
Robotic controls
Speech signal input and output
Text-to-Speech (TTS) is used to
Decode images
Translate languages
Edit videos
Convert text into spoken output
Concatenative synthesis in TTS involves
Rule learning
Voice modulation
Sentiment scoring
Joining pre-recorded voice clips
Parametric speech synthesis uses
Rule-based mappings
Optical scanners
Image renderers
Mathematical models for pitch/duration
Neural speech synthesis uses
Wired networks
Static rules
Symbolic logic
Deep learning models
The earliest idea of mimicking human intelligence via machines came in
2000
1994
1850
1949
One of the first successful machine translation attempts was
Siri
Google Maps
Whisper AI
Georgetown-IBM experiment
SHRDLU was designed to
Analyze news articles
Summarize novels
Recognize faces
Manipulate blocks via language
PCFG in NLP stands for
Parsed Context Feature Graph
Phase Check Flow Generator
Primary Character Frequency Generator
Probabilistic Context-Free Grammar
Word2Vec represents words as
Audio clips
Binary trees
Random pixels
Dense vectors
Google’s GNMT stands for
General Neural Math Tool
Geo NLP Mapping Tracker
Graphics NLP Metric Tester
Google Neural Machine Translation
BERT is a type of
Rule-based algorithm
Classical syntax checker
Grammar parser
Transformer-based language model
GPT-3 was released in
2010
2013
2017
2020
Which of these is used in ASR systems?
Rendering engine
Color filter
Motion detector
Acoustic model
A language model in ASR is used to
Style text
Highlight words
Create visuals
Predict next word sequences
Speaker variability refers to
Browser differences
File formats
Keyboard layouts
Differences in speech due to accent/pitch etc.
Noise robustness in ASR is the ability to
Change UI
Display graphics
Speed up translation
Recognize speech despite background noise
Which of the following is not an NLP application?
Chatbot
Speech recognition
Text summarization
Image classification
NLP can be used in business to
Replace cashiers
Improve road safety
Summarize text reports
Automate customer service chat
NLP is useful in healthcare for
Scanning retina
Detecting tumors
Managing glucose levels
Analyzing doctor-patient conversations
In NLP, the term “tag” often refers to
Video subtitles
Font styles
Filter types
Label assigned to text (like positive/negative)
Tokenization is the process of
Encrypting passwords
Compressing images
Formatting files
Splitting text into smaller units
An acoustic model maps
Text to graphs
HTML to CSS
Speech to colors
Sound to phonetic units
The ultimate goal of NLP is to
Replace human teachers
Design faster processors
Increase memory size
Help computers understand and generate human language
