Natural Language Processing

What is the main focus of Natural Language Processing (NLP)?

• data analysis
• language understanding and generation
• robotics
• image processing

Natural Language Understanding involves:

• interpreting meaning from text
• creating visual content
• generating new languages
• translating languages

Natural Language Generation is about:

• generating language from knowledge
• decoding encrypted messages
• learning new alphabets
• understanding spoken languages

The meaning of words in language is often considered:

• tricky due to context-dependence
• fixed and unambiguous
• based solely on morphology
• dependent on syntax only

Morphology in language processing helps in understanding words by looking at:

• their origin
• the volume at which they are spoken
• the letters they share
• their emotional tone

Distributional Semantics is used to:

• translate words into different languages
• guess similar meanings based on context
• understand word pronunciation
• measure the volume of words

Count Vectors represent:

• how many times words appear with others
• the alphabetical order of words
• the emotional tone of words
• the number of syllables in words

What challenge is associated with using count vectors in NLP?

• they cannot process written text
• they need a massive amount of data storage
• they only work for English
• they require understanding the tone of voice

Encoder-decoder models in NLP are used to:

• build internal representations for language
• translate words into Morse code
• create graphical interfaces
• encode audio into text

Language modeling in NLP aims to:

• predict the next word in a sentence
• model new languages
• teach languages to children
• generate musical lyrics