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Semantics in NLP (part 2)

Semantics in NLP (part 2). MAS.S60 Rob Speer Catherine Havasi. * Lots of slides borrowed for lots of sources! See end. . Are people doing logic?. Language Log: “Russia sentences” *More people have been to Russia than I have. Are people doing logic?. Language Log: “Russia sentences”

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Semantics in NLP (part 2)

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  1. Semantics in NLP(part 2) MAS.S60 Rob Speer Catherine Havasi * Lots of slides borrowed for lots of sources! See end.

  2. Are people doing logic? • Language Log: “Russia sentences” • *More people have been to Russia than I have.

  3. Are people doing logic? • Language Log: “Russia sentences” • *More people have been to Russia than I have. • *It just so happens that more people are bitten by New Yorkers than they are by sharks.

  4. Are people doing logic? • The thing is, is people come up with new ways of speaking all the time.

  5. More lexical semantics

  6. Quantifiers • Every/all: \P. \Q. all x. (P(x) -> Q(x)) • A/an/some: \P. \Q. exists x. (P(x) & Q(x)) • The: • \P. \Q. Q(x) • P(x) goes in the presuppositions

  7. High-level overview of C&C • Find the highest-probability result with coherent semantics • Doesn’t this create billions of parses that need to be checked? • Yes.

  8. High-level overview of C&C • Parses using a Combinatorial Categorial Grammar (CCG) • fancier than a CFG • includes multiple kinds of “slash rules” • lots of grad student time spent transforming Treebank • MaxEnt “supertagger” tags each word with a semantic category

  9. High-level overview of C&C • Find the highest-probability result with coherent semantics • Doesn’t this create billions of parses that need to be checked?

  10. High-level overview of C&C • Find the highest-probability result with coherent semantics • Doesn’t this create millions of parses that need to be checked? • Yes. A typical sentence uses 25 GB of RAM. • That’s where the Beowulf cluster comes in.

  11. Can we do this with NLTK? • NLTK’s feature-based parser has some machinery for doing semantics

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