Fall 2004 Lecture Notes #2 EECS 595 / LING 541 / SI 661 Natural Language Processing

Course logistics Instructor: Prof. Dragomir Radev Class times: Tu 1:10-3:55 PM, in 412, WH Office hours: M 10-11, Tu in 3080, WH Home page:

Regular Expressions and Automata

Regular expressions Searching for “woodchuck” Searching for “woodchucks” with an optional final “s” Regular expressions Finite-state automata (singular: automaton)

Regular expressions Basic regular expression patterns Perl-based syntax (slightly different from other notations for regular expressions) Disjunctions [abc] Ranges [A-Z] Negations [^Ss] Optional characters ? and * Wild cards. Anchors ^ and $, also \b and \B Disjunction, grouping, and precedence |

Writing correct expressions Exercise: write a Perl regular expression to match the English article “the”: /the/ /[tT]he/ /\b[tT]he\b/ /[^a-zA-Z][tT]he[^a-zA-Z]/ /(^|[^a-zA-Z])[tT]he[^a-zA-Z]/

A more complex example Exercise: Write a regular expression that will match “any PC with more than 500MHz and 32 Gb of disk space for less than $1000”: /$[0-9]+/ /$[0-9]+\.[0-9][0-9]/ /\b$[0-9]+(\.[0-9][0-9])?\b/ /\b[0-9]+ *(MHz|[Mm]egahertz|Ghz| [Gg]igahertz)\b/ /\b[0-9]+ *(Mb|[Mm]egabytes?)\b/ /\b[0-9](\.[0-9]+) *(Gb|[Gg]igabytes?)\b/

Advanced operators

Substitutions and memory Substitutions Memory ( \1, \2, etc. refer back to matches) s/colour/color/ s/([0-9]+)/ /

Eliza [Weizenbaum, 1966] User: Men are all alike ELIZA: IN WHAT WAY User: They’re always bugging us about something or other ELIZA: CAN YOU THINK OF A SPECIFIC EXAMPLE? User: Well, my boyfriend made me come here ELIZA: YOUR BOYFRIEND MADE YOU COME HERE User: He says I’m depressed much of the time ELIZA: I AM SORRY TO HEAR THAT YOU ARE DEPRESSED

Eliza-style regular expressions s/.* YOU ARE (depressed|sad).*/I AM SORRY TO HEAR YOU ARE \1/ s/.* YOU ARE (depressed|sad).*/WHY DO YOU THINK YOU ARE \1/ s/.* all.*/IN WHAT WAY/ s/.* always.*/CAN YOU THINK OF A SPECIFIC EXAMPLE/ Step 1: replace first person references with second person references Step 2: use additional regular expressions to generate replies Step 3: use scores to rank possible transformations

Finite-state automata Finite-state automata (FSA) Regular languages Regular expressions

Finite-state automata (machines) baa! baaa! baaaa! baaaaa!... q0q0 q1q1 q2q2 q3q3 q4q4 baa! a baa+! state transition final state

Input tape aba!b q0q0

Finite-state automata Q: a finite set of N states q 0, q 1, … q N  : a finite input alphabet of symbols q 0 : the start state F: the set of final states  (q,i): transition function

State-transition tables Input Stateba!

The FSM toolkit and friends Developed at AT&T Research (Riley, Pereira, Mohri, Sproat) Download: Tutorial available 4 useful parts: FSM, Lextools, GRM, Dot (separate) –/clair3/tools/fsm-3.6/bin –/clair3/tools/lextools/bin –/clair3/tools/dot/bin

D-RECOGNIZE function D-RECOGNIZE (tape, machine) returns accept or reject index  Beginning of tape current-state  Initial state of machine loop if End of input has been reached then if current-state is an accept state then return accept else return reject elsif transition-table [current-state, tape[index]] is empty then return reject else current-state  transition-table [current-state, tape[index]] index  index + 1 end

Adding a failing state q0q0 q1q1 q2q2 q3q3 q4q4 baa! a qFqF a ! b ! b! b b a !

Languages and automata Formal languages: regular languages, non-regular languages deterministic vs. non-deterministic FSAs Epsilon (  ) transitions

Using NFSAs to accept strings Backup: add markers at choice points, then possibly revisit underexplored markers Look-ahead: look ahead in input Parallelism: look at alternatives in parallel

Using NFSAs Input Stateba!  ,

More about FSAs Transducers Equivalence of DFSAs and NFSAs Recognition as search: depth-first, breadth- search

Recognition using NFSAs

Regular languages Operations on regular languages and FSAs: concatenation, closure, union Properties of regular languages (closed under concatenation, union, disjunction, intersection, difference, complementation, reversal, Kleene closure)

An exercise J&M 2.8. Write a regular expression for the language accepted by the NFSA in the Figure.

Morphology and Finite-State Transducers

Morphemes Stems, affixes Affixes: prefixes, suffixes, infixes: hingi (borrow) – humingi (agent) in Tagalog, circumfixes: sagen – gesagt in German Concatenative morphology Templatic morphology (Semitic languages) : lmd (learn), lamad (he studied), limed (he taught), lumad (he was taught)

Morphological analysis rewrites unbelievably

Inflectional morphology Tense, number, person, mood, aspect Five verb forms in English 40+ forms in French Six cases in Russian: Up to 40,000 forms in Turkish (you cause X to cause Y to … do Z)

Derivational morphology Nominalization: computerization, appointee, killer, fuzziness Formation of adjectives: computational, embraceable, clueless

Finite-state morphological parsing Cats: cat +N +PL Cat: cat +N +SG Cities: city +N +PL Geese: goose +N +PL Ducks: (duck +N +PL) or (duck +V +3SG) Merging: +V +PRES-PART Caught: (catch +V +PAST-PART) or (catch +V +PAST)

Principles of morphological parsing Lexicon Morphotactics (e.g., plural follows noun) Orthography (easy  easier) Irregular nouns: e.g., geese, sheep, mice Irregular verbs: e.g., caught, ate, eate

FSA for adjectives Big, bigger, biggest Cool, cooler, coolest, coolly Red, redder, reddest Clear, clearer, clearest, clearly, unclear, unclearly Happy, happier, happiest, happily Unhappy, unhappier, unhappiest, unhappily What about: unbig, redly, and realest?

Using FSA for recognition Is a string a legitimate word or not? Two-level morphology: lexical level + surface level (Koskenniemi 83) Finite-state transducers (FST) – used for regular relations Inversion and composition of FST

Orthographic rules Beg/begging Make/making Watch/watches Try/tries Panic/panicked

Combining FST lexicon and rules Cascades of transducers: the output of one becomes the input of another

Weighted Automata

Phonetic symbols IPA Arpabet Examples

Using WFST for language modeling Phonetic representation Part-of-speech tagging

Word Classes and Part Of Speech Tagging

Some POS statistics Preposition list from COBUILD Single-word particles Conjunctions Pronouns Modal verbs

Tagsets for English Penn Treebank Other tagsets (see Week 1 slides)

POS ambiguity Degrees of ambiguity (DeRose 1988) Rule-based POS tagging –ENGTWOL (Voutilainen et al. ) –Sample rule: Adverbial-That rule (“it isn’t that odd”) (“Given input: “that” if (+1 A/ADV/QUANT); (+2 SENT-LIM); (NOT –1 SVOC/A); (not a verb like “consider”) then eliminate non-ADV tags else eliminate ADV tag

Evaluating POS taggers Percent correct What is the lower bound on a system’s performance? What about the upper bound?

Kappa N: number of items (index i) n: number of categories (index j) k: number of annotators when  >.8 – agreement is considered high

Readings for next time J&M Chapters 5.9, 8, 9 Lecture notes #2