fall 2004 lecture notes #4 eecs 595 / ling 541 / si 661 natural language processing

Fall 2004

Lecture Notes #4

EECS 595 / LING 541 / SI 661

Natural Language Processing

Parsing withContext-Free Grammars

Introduction

• Parsing = associating a structure (parse tree) to an input string using a grammar

• CFG are declarative, they don’t specify how the parse tree will be constructed

• Parse trees are used in grammar checking, semantic analysis, machine translation, question answering, information extraction

• Example: “How many people in the Human Resources Department receive salaries above $30,000?”

Parsing as search

S NP VP Det that | this |a

S Aux NP VP Noun book | flight | meal | money

S VP Verb book | include | prefer

NP Det Nominal Aux does

Nominal Noun Proper-Noun Houston | TWA

Nominal Noun Nominal Prep from | to | on

NP Proper-Noun

VP Verb

VP Verb NP

Nominal Nominal PP

Parsing as search

Book that flight. S

VP

NP

Nom

Verb Det Noun

Book that flight

Two types of constraints on the parses: a) some that come from the input string,b) others that come from the grammar

Top-down parsing

S

NP VP

S

Aux VP

S

VP

S

NP

S

NP VP

Det Nom

S

NP VP

PropN

S

NP VP

Det Nom

S

VP

V NP

Aux

S

NP VPAux

PropN

S

VP

V

Book that flight

Book that flight

Noun Det Noun

Book that flight

Verb Det Noun

Book that flight

Noun Det Noun

Book that flight

Verb Det Noun

Book that flight

Noun Det Noun

Book that flight

Verb Det Noun

Book that flight

Verb Det Noun

Book that flight

Verb Det Noun

Book that flight

Verb Det Noun

NOM NOM NOM

NOMNOM NOM NOM

NOM NOM

VP NP

NP NP

VP

Bottom-up parsing

NP

VP

Comparing TD and BU parsers

• TD never wastes time exploring trees that cannot result in an S.

• BU however never spends effort on trees that are not consistent with the input.

• Needed: some middle ground.

Basic TD parser

• Practically infeasible to generate all trees in parallel.

• Use depth-first strategy.

• When arriving at a tree that is inconsistent with the input, return to the most recently generated but still unexplored tree.

fig10.05.pdf

function TOP-DOWN-PARSE (input, grammar) returns a parse tree agenda (Initial S tree, Beginning of input) current-search-state POP (agenda) loop if SUCCESSFUL-PARSE? (current-search-state) then return TREE (current-search-state) else if CAT (NODE-TO-EXPAND (current-search-state)) is a POS then if CAT (node-to-expand) POS (CURRENT-INPUT (current-search-state)) then PUSH (APPLY-LEXICAL-RULE (current-search-state), agenda) else return reject else PUSH (APPLY-RULES (current-search-state, grammar), agenda) if agenda is empty then return reject else current-search-state NEXT (agenda) end

A TD-DF-LR parser

An example

Does this flight include a meal?

fig10.07.pdf fig10.08.pdf fig10.09.pdf

Problems with the basic parser

• Left-recursion: rules of the type: NP NP PPsolution: rewrite each rule of the form A A | using a new symbol: A A’ A A’ |

• Ambiguity: attachment ambiguity, coordination ambiguity, noun-phrase bracketing ambiguity

• Attachment ambiguity: I saw the Grand Canyon flying to New York

• Coordination ambiguity: old men and women

Problems with the basic parser

• Example:President Kennedy today pushed aside other White House business to devote all his time and attention to working on the Berlin crisis address he will deliver tomorrow night to the American people over nationwide television and radio.

• Solutions: return all parses or include disambiguation in the parser.

• Inefficient reparsing of subtrees: a flight from Indianapolis to Houston on TWA

fig10.10.pdf fig10.11.pdf fig10.12.pdf fig10.13.pdf

The Earley algorithm

• Resolving:– Left-recursive rules– Ambiguity– Inefficient reparsing of subtrees

• A chart with N+1 entries• Dotted rules

– S . VP, [0,0]

– NP Det . Nominal, [1,2]

– VP V NP ., [0,3]fig10.14.pdf fig10.15.pdf fig10.16.pdf fig10.17.pdf

Parsing with FSAs

• Shallow parsing

• Useful for information extraction: noun phrases, verb phrases, locations, etc.

• The Fastus system (Appelt and Israel, 1997)

• Sample rules for noun groups:NG Pronoun | Time-NP | Date-NPNG (DETP) (Adjs) HdNns | DETP Ving HdNnsDETP DETP-CP | DETP-CP

• Complete determiner-phrases: “the only five”, “another three”, “this”, “many”, “hers”, “all”, “the most”

fig10.19.pdffig10.18.pdf fig10.20.pdf fig10.21.pdf

Sample FASTUS outputCompany Name: Bridgestone Sports Co.Verb Group: saidNoun Group: FridayNoun Group: itVerb Group: had set upNoun Group: a joint venturePreposition: inLocation: TaiwanPreposition: withNoun Group: a local concernConjunction: andNoun Group: a Japanese trading houseVerb Group: to produceNoun Group: golf clubsVerb Group: to be shippedPreposition: toLocation: Japan

Features and unification

Introduction

• Grammatical categories have properties• Constraint-based formalisms• Example: this flights: agreement is difficult to

handle at the level of grammatical categories• Example: many water: count/mass nouns• Sample rule that takes into account features: S

NP VP (but only if the number of the NP is equal to the number of the VP)

Feature structuresCAT NPNUMBER SINGULARPERSON 3

CAT NP

AGREEMENT NUMBER SG PERSON 3

Feature paths: {x agreement number}

Unification

[NUMBER SG] [NUMBER SG] +

[NUMBER SG] [NUMBER PL] -

[NUMBER SG] [NUMBER []] = [NUMBER SG]

[NUMBER SG] [PERSON 3] = ?

Agreement

• S NP VP{NP AGREEMENT} = {VP AGREEMENT}

• Does this flight serve breakfast?• Do these flights serve breakfast?

• S Aux NP VP{Aux AGREEMENT} = {NP AGREEMENT}

Agreement

• These flights• This flight

• NP Det Nominal{Det AGREEMENT} = {Nominal AGREEMENT}

• Verb serve{Verb AGREEMENT NUMBER} = PL

• Verb serves{Verb AGREEMENT NUMBER} = SG

Subcategorization

• VP Verb{VP HEAD} = {Verb HEAD}{VP HEAD SUBCAT} = INTRANS

• VP Verb NP{VP HEAD} = {Verb HEAD}{VP HEAD SUBCAT} = TRANS

• VP Verb NP NP{VP HEAD} = {Verb HEAD}{VP HEAD SUBCAT} = DITRANS

Readings for next time

• J&M Chapters 12, 13, 20

• Lecture notes #4

• FUF/CFUF documentation