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