1. CS 4705 Lecture 2 Regular Expressions and Automata

2. Representations and Algorithms for NLP Representations: formal models used to capture linguistic knowledge Algorithms manipulate representations to analyze or generate linguistic phenomena Simplest often produce best performance but….the 80/20 Rule and “low-hanging fruit”

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

4. NLP Algorithms Most are parsers or transducers: accept or reject input, and construct new structure from input –State space search Pair a partial structure with a part of the inputpartial structure Spaces too big and ‘best’ is hard to define –Dynamic programming Avoid recomputing structures that are common to multiple solutions

5. DetNom NP thecat The cat is on the mat

6. Today Review some of the simple representations and ask ourselves how we might use them to do interesting and useful things –Regular Expressions –Finite State Automata

7. Uses of Regular Expressions in NLP As grep, perl: Simple but powerful tools for large corpus analysis and ‘shallow’ processing –What word is most likely to begin a sentence? –What word is most likely to begin a question? –In your own email, are you more or less polite than the people you correspond with? With other unix tools, allow us to –Obtain word frequency and co-occurrence statistics –Build simple interactive applications (e.g. Eliza) Regular expressions define regular languages or sets

8. Some Examples

9. REDescriptionUses? /a*/Zero or more a’sOptional doubled modifiers (words) /a+/One or more a’sNon-optional... /a?/Zero or one a’sOptional... /cat|dog/‘cat’ or ‘dog’Words modifying pets /^cat$/A line containing only ‘cat’ ?? /\bun\B/Beginnings of longer strings Words prefixed by ‘un’

10. happier and happier, fuzzier and fuzzier / (.+)ier and \1ier / Morphological variants of ‘puppy’/pupp(y|ies)/ E.G.RE

11. Substitutions (Transductions) Sed or ‘s’ operator in Perl –s/regexp1/pattern/ –s/I am feeling (.++)/You are feeling \1?/ –s/I gave (.+) to (.+)/Why would you give \2 \1?/

12. Examples Predictions from a news corpus: –Which candidate for Governor is mentioned most often in the news? Is going to win? –What stock should you buy? –Which White House advisers have the most power? Language use: –Which form of comparative is more frequent: ‘oftener’ or ‘more often’? –Which pronouns are conjoined most often? –How often do sentences end with infinitival ‘to’? –What words most often begin and end sentences? –What’s the most common word in your email? Is it different from your neighbor?

13. Personality profiling: –Are you more or less polite than the people you correspond with? –With labeled data, which words signal friendly msgs vs. unfriendly ones?

14. Finite State Automata FSAs recognize the regular languages represented by regular expressions –SheepTalk: /baa+!/ q0 q4 q1q2q3 ba a a! Directed graph with labeled nodes and arc transitions Five states: q0 the start state, q4 the final state, 5 transitions

15. Formally FSA is a 5-tuple consisting of –Q: set of states {q0,q1,q2,q3,q4} –  : an alphabet of symbols {a,b,!} –q0: a start state –F: a set of final states in Q {q4} –  (q,i): a transition function mapping Q x  to Q q0 q4 q1q2q3 ba a a!

16. FSA recognizes (accepts) strings of a regular language –baa! –baaa! –… Tape metaphor: a rejected input aba!b

17. State Transition Table for SheepTalkSheepTalk State Input ba! 0100 1020 2030 3034 4000

18. Non-Deterministic FSAs for SheepTalk q0 q4 q1q2q3 ba a a! q0 q4 q1q2q3 baa! 

19. FSAs as Grammars for Natural Language q2q4q5q0q3q1q6 therev mr dr hon patl.robinson ms mrs  Can you use a regexpr to capture this too?

20. Problems of Non-Determinism ‘Natural’….but at any choice point, we may follow the wrong arc Potential solutions: –Save backup states at each choice point –Look-ahead in the input before making choice –Pursue alternatives in parallel –Determinize our NFSAs (and then minimize) FSAs can be useful tools for recognizing – and generating – subsets of natural language –But they cannot represent all NL phenomena (The mouse the cat chased died.)

21. Summing Up Regular expressions and FSAs can represent subsets of natural language as well as regular languages –Both representations may be impossible for humans to understand for any real subset of a language –But they are very easy to use for smaller subsets Next time: Read Ch 3 (1-2,5) For fun: –Think of ways you might characterize your email using only regular expressions –Check over Homework 1Homework 1