1. Natural Language Processing (NLP)
Lecture No 5
Institute of Southern Punjab Multan
Department of Computer Science

2. Basic Text Processing
Regular Expression

3. Basic Process of NLU Phonological / morphological analyser
Spoken input
For speech understanding
Phonological / morphological analyser
Phonological & morphological rules
Sequence of words
SYNTACTIC COMPONENT
Grammatical Knowledge
Indicating relns (e.g., mod) between words
Syntactic structure (parse tree)
Thematic Roles
SEMANTIC INTERPRETER
Semantic rules, Lexical semantics
Selectional restrictions
Logical form
CONTEXTUAL REASONER
Pragmatic & World Knowledge
Meaning Representation

4. NLP Representations State Machines Rule Systems Logic-based Formalisms
FSAs, FSTs, HMMs, ATNs, RTNs
Rule Systems
CFGs, Unification Grammars, Probabilistic CFGs
Logic-based Formalisms
1st Order Predicate Calculus, Temporal and other Higher Order Logics
Models of Uncertainty
Bayesian Probability Theory

5. Regular Expressions Can be viewed as a way to specify:
Search patterns over text string
Design of a particular kind of machine, called a Finite State Automaton (FSA)
These are really equivalent

6. Regular Expressions
Regular Expression: Formula in algebraic notation for specifying a set of strings
String: Any sequence of alphanumeric characters
Letters, numbers, spaces, tabs, punctuation marks
Regular Expression Search
Pattern: specifying the set of strings we want to search for
Corpus: the texts we want to search through

7. Regular Expressions The “foundational” operations
Concatenation abc abc Disjunction a|b a b (a|bb)d ad bbd Kleene star a* ε a aa aaa ... c(a|bb)* ca cbba Regular expressions / Finite-state automata are “closed under these operations” Pattern Matches The empty string

8. Practical Applications of RegEx’s
• Web search • Word processing, find, substitute • Validate fields in a database (dates, addr, URLs) • Searching corpus for linguistic patterns – and gathering stats...

9. Regular expressions A formal language for specifying text strings
How can we search for any of these?
woodchuck
woodchucks
Woodchuck
Woodchucks

10. Regular Expressions: Disjunctions
Letters inside square brackets []
Ranges [A-Z]
Pattern
Matches
[wW]oodchuck
Woodchuck, woodchuck
[ ]
Any digit
Pattern
Matches
[A-Z]
An upper case letter
Drenched Blossoms
[a-z]
A lower case letter
my beans were impatient
[0-9]
A single digit
Chapter 1: Down the Rabbit Hole

11. Regular Expressions: Negation in Disjunction
Negations [^Ss]
Carat means negation only when first in []
Pattern
Matches
[^A-Z]
Not an upper case letter
Oyfn pripetchik
[^Ss]
Neither ‘S’ nor ‘s’
I have no exquisite reason”
[^e^]
Neither e nor ^
Look here
a^b
The pattern a carat b
Look up a^b now

12. Regular Expressions: More Disjunction
Woodchucks is another name for groundhog!
The pipe | for disjunction
Pattern
Matches
groundhog|woodchuck
yours|mine
yours mine
a|b|c
= [abc]
[gG]roundhog|[Ww]oodchuck
Photo D. Fletcher

13. Regular Expressions: ? * + .
Pattern
Matches
colou?r
Optional previous char
color colour
oo*h!
0 or more of previous char
oh! ooh! oooh! ooooh!
o+h!
1 or more of previous char
baa+
baa baaa baaaa baaaaa
beg.n
begin begun begun beg3n
Stephen C Kleene
Kleene *, Kleene +

14. Regular Expressions: Anchors ^ $
Pattern
Matches
^[A-Z]
Palo Alto
^[^A-Za-z]
1 “Hello”
\.$
The end. .$
The end? The end!

15. Some Examples
Note: [^ - is not – i.e., at the beginning of a disjunction. Later in the disjunction it really means the ^.

16. Morphological variants of ‘puppy’RE
Description
Uses?
/a*/
Zero or more a’s
Optional doubled modifiers
/a+/
One or more a’s
Non-optional...
/a?/
Zero or one a’s
Optional...
/cat|dog/
‘cat’ or ‘dog’
Words modifying pets
/^cat\.$/
A line that contains only cat. ^anchors beginning, $ anchors end of line. ??
/\bun\B/
Beginnings of longer strings
Words prefixed by ‘un’
/pupp(y|ies)/
Morphological variants of ‘puppy’
Note: yet another use of ^ but this time in a different context… it means to start a line!
\b matches a word boundary; \B matches a word-non-boundary – a word is a sequence of digits, underscores, letters
/ (.+)ier and \1ier /
happier and happier, fuzzier and fuzzier

17. Example Find me all instances of the word “the” in a text. the
Misses capitalized examples
[tT]he
Incorrectly returns other or theology
[^a-zA-Z][tT]he[^a-zA-Z]

18. Optionality and Repetition
/[Ww]oodchucks?/ matches woodchucks, Woodchucks, woodchuck, Woodchuck
/colou?r/ matches color or colour
/he{3}/ matches heee
/(he){3}/ matches hehehe
/(he){3,} matches a sequence of at least 3 he’s

19. Operator Precedence Hierarchy
1. Parentheses ()
2. Counters * + ? {}
3. Sequence of Anchors the ^my end$
4. Disjunction |
Examples
/moo+/
/try|ies/
/and|or/
Because counters have a higher precidence than sequence, this matches mooooooo.
The second one probably does not do what is expected – would match try or ies, but not tries.
The third does work as expected.

20. A Simple Exercise /the/ /[tT]he/ /\b[tT]he\b/
Write a regular expression to find all instances of the determiner “the”:
/the/
/[tT]he/
/\b[tT]he\b/
/(^|[^a-zA-Z][tT]he[^a-zA-Z]/
The recent attempt by the police to retain their current rates of pay has not gathered much favor with the southern factions.

21. A Simple Exercise /the/ /[tT]he/ /\b[tT]he\b/
Write a regular expression to find all instances of the determiner “the”:
/the/
/[tT]he/
/\b[tT]he\b/
The recent attempt by the police to retain their current rates of pay
has not gathered much favor with
southern factions.

22. A Simple Exercise /the/ /[tT]he/ /\b[tT]he\b/
Write a regular expression to find all instances of the determiner “the”:
/the/
/[tT]he/
/\b[tT]he\b/
/(^|[^a-zA-Z][tT]he[^a-zA-Z]/
The recent attempt by the police to retain their current rates of pay has not gathered much favor with the southern factions.

23. A Simple Exercise /the/ /[tT]he/ /\b[tT]he\b/
Write a regular expression to find all instances of the determiner “the”:
/the/
/[tT]he/
/\b[tT]he\b/
/(^|[^a-zA-Z][tT]he[^a-zA-Z]/
The recent attempt by the police to retain their current rates of pay has not gathered much favor with the southern factions.

24. A Simple Exercise /the/ /[tT]he/ /\b[tT]he\b/
Write a regular expression to find all instances of the determiner “the”:
/the/
/[tT]he/
/\b[tT]he\b/
/(^|[^a-zA-Z][tT]he[^a-zA-Z]/
The recent attempt by the police to retain their current rates of pay has not gathered much favor with the southern factions.

25. A Simple Exercise /the/ /[tT]he/ /\b[tT]he\b/
Write a regular expression to find all instances of the determiner “the”:
/the/
/[tT]he/
/\b[tT]he\b/
/(^|[^a-zA-Z])[tT]he[^a-zA-Z]/
The recent attempt by the police to retain their current rates of pay has not gathered much favor with the southern factions.
The \b counts words as any sequence of digits, undercores, or letters. Suppose you wanted to find “the” in the context of _the or the28.
Could you write that one.
Last one: either it starts a line, or the thing in front of it is not a letter; followed by either t or T; followed by he where the following character is not a letter.

26. Errors
The process we just went through was based on fixing two kinds of errors
Matching strings that we should not have matched (there, then, other)
False positives (Type I)
Not matching things that we should have matched (The)
False negatives (Type II)

27. Errors cont. In NLP we are always dealing with these kinds of errors.
Reducing the error rate for an application often involves two antagonistic efforts:
Increasing accuracy or precision (minimizing false positives)
Increasing coverage or recall (minimizing false negatives).

28. Summary Regular expressions play a surprisingly large role
Sophisticated sequences of regular expressions are often the first model for any text processing text
For many hard tasks, we use machine learning classifiers
But regular expressions are used as features in the classifiers
Can be very useful in capturing generalizations