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

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

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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.

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

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More lexical semantics

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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

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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.

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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

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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?

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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.

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

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