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Semantic Analysis in NLP

Semantic Analysis

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Semantic Analysis in NLP

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  1. Natural Language Processing Lecture Notes 11 Chapter 15 (part 1)

  2. Semantic Analysis • These notes: syntax driven compositional semantic analysis • Assign meanings based only on the grammar and lexicon (no inference, ignore context)

  3. Compositional Analysis • Principle of Compositionality • The meaning of a whole is derived from the meanings of the parts • What parts? • The constituents of the syntactic parse of the input

  4. Example • AyCaramba serves meat

  5. Compositional Analysis

  6. Augmented Rules • We’ll accomplish this by attaching semantic formation rules to our syntactic CFG rules • Abstractly • This should be read as the semantics we attach to A can be computed from some function applied to the semantics of A’s parts.

  7. Easy parts… NP -> PropNoun NP -> MassNoun PropNoun -> AyCaramba MassNoun -> meat Attachments {PropNoun.sem} {MassNoun.sem} {AyCaramba} {MEAT} Example

  8. S -> NP VP VP -> Verb NP Verb -> serves {VP.sem(NP.sem)} {Verb.sem(NP.sem)} ??? Example

  9. Example

  10. Example

  11. Example

  12. Example

  13. Key Points • Each node in a tree corresponds to a rule in the grammar • Each grammar rule has a semantic rule associated with it that specifies how the semantics of the RHS of that rule can be computed from the semantics of its daughters.

  14. Quantified Phrases • Consider A restaurant serves meat. • Assume that A restaurant looks like • If we do the normal lambda thing we get

  15. Complex Terms • Allow the compositional system to pass around representations like the following as objects with parts: Complex-Term → <Quantifier var body>

  16. Example • Our restaurant example winds up looking like

  17. Conversion • So… complex terms wind up being embedded inside predicates. So pull them out and redistribute the parts in the right way… P(<quantifier, var, body>) turns into Quantifier var body connective P(var)

  18. Example

  19. Quantifiers and Connectives • If the quantifier is an existential, then the connective is an ^ (and) • If the quantifier is a universal, then the connective is an -> (implies)

  20. Multiple Complex Terms • Note that the conversion technique pulls the quantifiers out to the front of the logical form… • That leads to ambiguity if there’s more than one complex term in a sentence.

  21. Quantifier Ambiguity • Consider • Every restaurant has a menu • That could mean that every restaurant has a menu • Or that There’s some uber-menu out there and all restaurants have that menu

  22. Quantifier Scope Ambiguity

  23. Ambiguity • Much like the prepositional phrase attachment problem • The number of possible interpretations goes up exponentially with the number of complex terms in the sentence • The best we can do: weak methods to prefer one interpretation over another

  24. Integration with a Parser • Assume you’re using a dynamic-programming style parser (Earley or CYK). • As with feature structures for agreement and subcategorization, we add semantic attachments to states. • As constituents are completed and entered into the table, we compute their semantics.

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