Language

Question 1

spans all tasks where the AI gets human language as input

Answer 1

Natural Language Processing

Question 2

examples of tasks in Natural Language Processing

Answer 2

• automatic summarization
• information extraction
• language identification
• machine translation
• named entity recognition
• speech recognition
• text classification
• word sense disambiguation

Question 3

sentence structure

Answer 3

Syntax

Question 4

meaning of words or sentences

Answer 4

Semantics

Question 5

system of rules for generating sentences in a language

Answer 5

Formal Grammar

Question 6

text is abstracted from its meaning to represent the structure of the sentence using formal grammar

Answer 6

Context-Free Grammar

Question 7

a sequence of n items from a sample of text.

Answer 7

n-gram

Question 8

a contiguous sequence of n characters from a sample of text

Answer 8

character n-gram

Question 9

a contiguous sequence of n words from a sample of text

Answer 9

word n-gram

Question 10

a contiguous sequence of 1 item from a sample of text

Answer 10

unigram

Question 11

a contiguous sequence of 2 item from a sample of text

Answer 11

bigram

Question 12

a contiguous sequence of 3 item from a sample of text

Answer 12

trigrams

Question 13

task of splitting a sequence of characters into pieces (tokens)

Answer 13

Tokenization

Question 14

the task of splitting a sequence of characters into words

Answer 14

word tokenization

Question 15

the task of splitting a sequence of characters into sentences

Answer 15

sentence tokenization

Question 16

How to generate text using a Markov Model

Answer 16

Markov Models

Question 17

a model that represents text as an unordered collection of words.

Answer 17

Bag-of-words Model

Question 18

adding a value α to each value in our distribution to smooth the data

Answer 18

additive smoothing

Question 19

adds 1 to each value in our distribution, pretending that all values have been observed at least once.

Answer 19

Laplace Smoothing

Question 20

task of finding relevant documents in response to a user query.

Answer 20

Information retrieval

Question 21

models for discovering the topics for a set of documents

Answer 21

topic modeling

Question 22

counting how many times a term appears in a document.

Answer 22

term frequency

Question 23

words that have little meaning on their own, but are used to grammatically connect other words

Answer 23

function words

Question 24

am, by, do, is, which, with, yet,

Answer 24

function words

Question 25

words that carry meaning independently

Answer 25

content words

Question 26

algorithm, category, computer, ...

Answer 26

content words

Question 27

measure of how common or rare a word is across documents in a corpus

Answer 27

Inverse Document Frequency

Question 28

ranking of what words are important in a document by multiplying term frequency (TF) by inverse document frequency (IDF)

Answer 28

tf-idf

Question 29

task of extracting knowledge from documents

Answer 29

Information Extraction

Question 30

each word is represented with a vector that consists of as many values as we have words

Answer 30

One-Hot Representation

Question 31

meaning is distributed across multiple values in a vector.

Answer 31

Distributed Representation

Question 32

algorithm for generating distributed representations of words

Answer 32

word2vec

Question 33

a neural network architecture for predicting context given a target word

Answer 33

Skip-Gram Architecture