Lecture 2

Question 1

Tokenization

Answer 1

Process of bewakinf down the text into tokens (smaller chunks -words including punctutation)

Question 2

Each language has it is own stop words

Answer 2

true

Question 3

Stemming

Answer 3

Choping off letters from end of words until stem is reached

Question 4

Lancaster stemming

Answer 4

Considered with chopping off the word as much as possible (Sacrifice accuracy for speed)

Question 5

lemmatization

Answer 5

takes into conisderation root of word

Question 6

Lemmatizer allow you to consider the part of speech of a word unlike the stem (lemmtizing a noun is different than lmmetizing a verb)

Answer 6

True

Question 7

text is a list of sent which is seq of words

Answer 7

true

Question 8

Pattern something that repeats itslef - find and analyze repitions

Answer 8

true

Question 9

If you want to find what text about : first you need to find words that are repeaeted and that are not stop words

Answer 9

true

Question 10

Patterns is about words which conversation woll b centered

Answer 10

true

Question 11

Regular expressions

Answer 11

Language to find paterns

Question 12

Disjunction is the brackets and inside it is used as or

Answer 12

true

Question 13

[A-Z]

Answer 13

Any uppercase of the alphabet

Question 14

[^abc] means not small a not small b not c

Answer 14

true

Question 15

[a^b]

Answer 15

a or b

Question 16

^the

Answer 16

Searching for the pattern the

Question 17

abc?

Answer 17

means abc or ab

Question 18

[abc]?

Answer 18

means a or b or c

Question 19

• 0 or more occurences
  + 1 or more occurences
  . wild card (any

Answer 19

True

Question 20

+ is greedy

* is greedy

Answer 20

True

Question 21

$

Answer 21

at end

Question 22

search

Answer 22

search until finds first match

Question 23

Match and serch must be called with group to show result

Answer 23

true

Question 24

Split split based on char

Answer 24

true

Question 25

split can be like tokenize but it depends

Answer 25

tue

Question 26

Positive look ahead | What follow must match

Answer 26

Negatice look ahead | Will return if the following will noit match

Question 27

regex stemmer let u stem whatever u want

Answer 27

true

Question 28

You can always tokenize using regex

Answer 28

true

Question 29

Dynamic programming

Answer 29

tabular computation

Question 30

Minimum edit distance

Answer 30

Measuring how similar two strings are

Question 31

an edit may be deletion addition

Answer 31

true

Question 32

Minimum edit distance

Answer 32

typo is least far

Question 33

If two DNA can align implies they come from same source

Answer 33

true

Question 34

Algin

Answer 34

exact the same