Semantics in NLP(part 2)

MAS.S60Rob 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”– *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.

Are people doing logic?

• The thing is, is people come up with new ways of speaking all the time.

More lexical semantics

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

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.

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

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?

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.

Can we do this with NLTK?

• NLTK’s feature-based parser has some machinery for doing semantics