1. Fall 2004 Lecture Notes #4 EECS 595 / LING 541 / SI 661 Natural Language Processing

2. Parsing with Context-Free Grammars

3. 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?”

4. Parsing as search S  NP VPDet  that | this |a S  Aux NP VPNoun  book | flight | meal | money S  VPVerb  book | include | prefer NP  Det NominalAux  does Nominal  NounProper-Noun  Houston | TWA Nominal  Noun NominalPrep  from | to | on NP  Proper-Noun VP  Verb VP  Verb NP Nominal  Nominal PP

5. Parsing as search Book that flight. S VP NP Nom VerbDetNoun Bookthatflight Two types of constraints on the parses: a) some that come from the input string, b) others that come from the grammar

6. Top-down parsing S NPVP S AuxVP S S NP S VP DetNom S NPVP PropN S NPVP DetNom S VP VNP Aux S NPVPAux PropN S VP V

7. Bookthatflight Bookthatflight NounDetNoun Bookthatflight VerbDetNoun Bookthatflight NounDetNoun Bookthatflight VerbDetNoun Bookthatflight NounDetNoun Bookthatflight VerbDetNoun Bookthatflight VerbDetNoun Bookthatflight VerbDetNoun Bookthatflight VerbDetNoun NOM VPNP VP Bottom-up parsing NP VP

8. 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.

9. 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.

10. 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

11. An example Does this flight include a meal?

12. Problems with the basic parser Left-recursion: rules of the type: NP  NP PP solution: 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

13. 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

14. 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]

15. 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-NP NG  (DETP) (Adjs) HdNns | DETP Ving HdNns DETP  DETP-CP | DETP-CP Complete determiner-phrases: “the only five”, “another three”, “this”, “many”, “hers”, “all”, “the most”

16. Sample FASTUS output Company Name:Bridgestone Sports Co. Verb Group:said Noun Group:Friday Noun Group:it Verb Group:had set up Noun Group:a joint venture Preposition:in Location:Taiwan Preposition:with Noun Group:a local concern Conjunction:and Noun Group:a Japanese trading house Verb Group:to produce Noun Group:golf clubs Verb Group:to be shipped Preposition:to Location:Japan

17. Features and unification

18. 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)

19. Feature structures CAT NP NUMBER SINGULAR PERSON 3 CAT NP AGREEMENT NUMBER SG PERSON 3 Feature paths: {x agreement number}

20. Unification [NUMBER SG] [NUMBER SG] + [NUMBER SG] [NUMBER PL] - [NUMBER SG] [NUMBER []] = [NUMBER SG] [NUMBER SG] [PERSON 3] = ?

21. 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}

22. Agreement These flights This flight NP  Det Nominal {Det AGREEMENT} = {Nominal AGREEMENT} Verb  serve {Verb AGREEMENT NUMBER} = PL Verb  serves {Verb AGREEMENT NUMBER} = SG

23. 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

24. Readings for next time J&M Chapters 12, 13, 20 Lecture notes #4 FUF/CFUF documentation