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Context-Free Parsing

Context-Free Parsing. Read J & M Chapter 10. Basic Parsing Facts. Parsing Formal Languages vs Natural Ones. Example Comparison. Formal language: 17 * 8 + 24 First, we define which interpretation we want.

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Context-Free Parsing

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  1. Context-Free Parsing Read J & M Chapter 10.

  2. Basic Parsing Facts

  3. Parsing Formal Languages vs Natural Ones

  4. Example Comparison • Formal language: • 17 * 8 + 24 • First, we define which interpretation we want. • Second, we exploit parsing techniques so that it is not even necessary to try multiple paths to get to that answer. • If a = 2 then if b = 3 then i := 7 else i := 9 • English: • I hit the boy with a bat.

  5. Ambiguity • Lexical category ambiguity: Type this report. • Structural ambiguity: • Attachment ambiguity: • I hit the boy with the bat. • I saw the Statue of Liberty flying over New York. • A Belgian furniture shop is offering special packages for divorced men who hate shopping in a country where half of all marriages end in a divorce after five years. • Coordination ambiguity: Would you like pancakes or bacon and eggs? • Noun phrase bracketing: I’d like Canadian bacon and eggs. • Quantifier scope ambiguity: All the boys read a book about politics.

  6. How Bad is the Ambiguity Problem? Consider: Show me the meal on flight 286 from SF to Denver and NY. Bracketings: How many are there?

  7. What’s the Fix? • There isn’t an easy one. Two approaches: • Semantic filtering • Representing families of parses, not individual ones.

  8. Ambiguity and Nondeterminism Imply Search • Two basic approaches: • Top down parsing • Bottom up parsing • Example: Book that flight.

  9. A Miniature Grammar

  10. A Top-Down, Recursive Descent Parser Def (S: S := Build(S, NP(), VP()); If success then return S; S := Build(S, Aux(), NP(), VP()); If success then return S; S := Build(S, VP()); If success then return S; S := Build(S, S(), Conj(), S()); If success then return S; Return fail) Def (NP: NP := Build(NP, Det(), Nom()) ; If success then return NP; NP := Build(NP, PropN() ) ; If success then return NP; NP := Build(NP, NP(), S()); If success then return NP; Return fail) Def (VP: VP := Build(VP, V()); If success then return VP; VP := Build(VP, V(), NP()); If success then return VP; Return fail) Def (PropN: PrN:= Lookup(“PropN”); If success then return PropN; Return fail) Def (V: V:= Lookup(“V”); If success then returnV; Return fail) Def (Det: Det:= Lookup(“Det”); If success then return Det; Return fail)

  11. Example Let’s try to parse: Lucy purred.

  12. Adding Bottom Up Filtering Consider the sentence: Listen to me. We know that we cannot use either of the first two S rules because listen cannot be on the left corner of either an NP or an Aux. Facts such as this can be compiled from the grammar and stored in a table that lists, for each constituent, those parts of speech that can form the left corner. Example using S: Left corners: S  NP VP Det, PropN S  Aux NP VP Aux S  VP V S  S Conj S

  13. Problems with Top-Down Parsers • Left recursive rules: Let’s try: • Cats and dogs make good pets. • Rewrite A  A  |  as A   A • A    A | • Ambiguity • We will only find the first parse. • Repeated parsing of subtrees: Imagine we’d solved the conjunction problem. Now consider: The large furry cat with the huge tail purred and the baby smiled.

  14. The Earley Algorithm Goal: find one or more edges from 0 to 3 labeled S.

  15. Could We Do it with an FSM? • Not the way we’ve been doing it because of the recursive rules. But the FSM idea may be useful if we do one of the following: • Just parse fragments of sentences. • Augment the FSM formalism to allow more powerful operations.

  16. Parsing Fragments • Example: noun phrases. Maybe we don’t need to handle: • Lawyers whose clients committed fraud • Lawyers who committed fraud • Clients whose lawyers committed fraud • But we do care about: The huge and friendly cat Then we could build an FSM like:

  17. Augmenting the FSM - Recursion A Recursive Transition Network has the same power as a PDA or a context-free parser. Here’s a fragment of an RTN that recognizes sentences: S Conj S

  18. Augmenting the FSM – Allow Actions Who did she say she saw ____ coming down the hill? OBJ  QWORD Augmented Transition Networks (ATNs)

  19. Demos A parser with a nice graphical interface: http://www.ling.helsinki.fi/~tapanain/dg/eng/demo.html A parser with machine readable output: http://www.lingsoft.fi/cgi-bin/engcg?snt=Who+did+you+say+you+saw%3F&h=on A shallow parser: http://ilk.kub.nl/cgi-bin/tstchunk/demo.pl Whatever you pick, it needs to connect with the modules that come before and after it.

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