CSCI 5832 Natural Language Processing Jim Martin Lecture 19 4 10 08 1 Today 4 1 More semantics Dealing with quantifiers Dealing with ambiguity 2 4 10 08 Example Even if this is the right tree what does that tell us about the meaning 3 4 10 08 1 Meaning Representations We re going to take the same basic approach to meaning that we took to syntax and morphology We re going to create representations of linguistic inputs that capture the meanings of those inputs But unlike parse trees and the like these representations aren t primarily descriptions of the structure of the inputs 4 4 10 08 Meaning Representations In most cases they re simultaneously descriptions of the meanings of utterances and of some potential state of affairs in some world 5 4 10 08 Meaning Representations What could this mean representations of linguistic inputs that capture the meanings of those inputs For us it means Representations that permit or facilitate semantic processing 6 4 10 08 2 Representational Schemes We re going to make use of First Order Logic FOL as our representational framework Not because we think it s perfect Many of the alternatives turn out to be either too limiting or They turn out to be notational variants 7 4 10 08 FOL Allows for The analysis of truth conditions Allows us to answer yes no questions Supports the use of variables Allows us to answer questions through the use of variable binding Supports inference Allows us to answer questions that go beyond what we know explicitly 8 4 10 08 Example Mary gave a list to John Giving Mary John List More precisely Gave conveys a three argument predicate The first arg is the subject The second is the recipient which is conveyed by the NP in the PP The third argument is the thing given conveyed by the direct object 9 4 10 08 3 Better Turns out this representation isn t quite as useful as it could be Giving Mary John List Better would be 10 4 10 08 Predicates The notion of a predicate just got more complicated In this example think of the verb VP providing a template like the following The semantics of the NPs and the PPs in the sentence plug into the slots provided in the template 11 4 10 08 Semantic Analysis Semantic analysis is the process of taking in some linguistic input and assigning a meaning representation to it There a lot of different ways to do this that make more or less or no use of syntax We re going to start with the idea that syntax does matter The compositional rule to rule approach 12 4 10 08 4 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 What could it mean for a part to have a meaning 13 4 10 08 Example AyCaramba serves meat 14 4 10 08 Compositional Analysis 15 4 10 08 5 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 16 4 10 08 Example Attachments Easy parts PropNoun sem NP PropNoun MassNoun sem NP MassNoun AyCaramba PropNoun AyCaramba MEAT MassMoun meat 17 4 10 08 Example S NP VP VP Verb NP Verb serves VP sem NP sem Verb sem NP sem 18 4 10 08 6 Lambda Forms A simple addition to FOL Take a FOPC sentence with variables in it that are to be bound Allow those variables to be bound by treating the lambda form as a function with formal arguments 19 4 10 08 Example 20 4 10 08 Example 21 4 10 08 7 Example 22 4 10 08 Example 23 4 10 08 Integration Two basic approaches Integrate semantic analysis into the parser assign meaning representations as constituents are completed Pipeline assign meaning representations to complete trees only after they re completed 24 4 10 08 8 Example From BERP I want to eat someplace near campus Two parse trees two meanings 25 4 10 08 Pros and Cons If you integrate semantic analysis into the parser as its running You can use semantic constraints to cut off parses that make no sense You assign meaning representations to constituents that don t take part in the correct most probable parse 26 4 10 08 Break New schedule is up Finish 18 today Next time WSD secs 20 1 through 20 5 Next week Chapter 22 Quiz Average was 43 out of 55 I ll go over it next time Next quiz 4 17 Covers 17 18 20 21 22 27 4 10 08 9 Quantifiers Unfortunately things get a bit more complicated when we start looking at more complicated NPs The previous examples simplified things by only dealing with constants FOL Terms That is things that can be plugged into FOL predicates What about A menu Every restaurant etc Not every waiter 28 4 10 08 Quantifers Every restaurant closed 29 4 10 08 Quantifiers Roughly every in an NP like this is used to stipulate something about every member of the class The NP is specifying the class And the VP is specifying the thing stipulate So the NP is a template like 30 4 10 08 10 Quantifiers But that s not combinable with anything so wrap a lambda around it 31 4 10 08 Rules 32 4 10 08 Example 33 4 10 08 11 Every Restaurant Closed 34 4 10 08 Problem Every restaurant has a menu 35 4 10 08 Next Time Underspecification 36 4 10 08 12
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