1 / 20

Syntax-Driven Semantics in Compositional Semantics

Learn about the process of computing meaning from syntactic structures and semantic representations in compositional semantics.

mittiep
Download Presentation

Syntax-Driven Semantics in Compositional Semantics

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Lecture 17 Semantic Analysis: Syntax-Driven Semantics CS 4705

  2. Review • Some representations of meaning: • First order logic • Frames • etc. • Some linguistically relevant categories we want to represent • Predicates, arguments, variables, quantifiers • Categories, events, time, aspect • Today: How can we compute meaning about these categories from these representations?

  3. Compositional Semantics • The meaning of the whole is made up of the meaning of its parts • George cooks. Dan eats. Dan is sick. • Cook(George) Eat(Dan) Sick(Dan) • If George cooks and Dan eats, Dan will get sick. (Cook(George) ^ eat(Dan))  Sick(Dan) Sick(Dan)  Cook(George) • Part of the meaning derives from the people and activities it’s about (predicates and arguments, or, nouns and verbs) and part from the way they are ordered and related grammatically: syntax

  4. Syntax-Driven Semantics S NP VP eat(Dan) Nom V N Dan eats • So….can we link up syntactic structures to a corresponding semantic representation to produce the ‘meaning’ of a sentence in the course of parsing it?

  5. Specific vs. General-Purpose Rules • We don’t want to have to specify for every possible parse tree what semantic representation it maps to • We want to identify general mappings from parse trees to semantic representations: • Again (as with feature structures) we will augment the lexicon and the grammar • Rule-to-rule hypothesis: a mapping exists between rules of the grammar and rules of semantic representation

  6. Semantic Attachments • Extend each grammar rule with instructions on how to map the components of the rule to a semantic representation (grammars are getting complex) S  NP VP {VP.sem(NP.sem)} • Each semantic function is defined in terms of the semantic representation of choice • Problem: how to define these functions and how to specify their composition so we always get the meaning representation we want from our grammar?

  7. A ‘Simple’ Example AyCaramba serves meat. • Associating constants with constituents • ProperNoun  AyCaramba {AyCaramba} • MassNoun  meat {Meat} • Defining functions to produce these from input • NP  ProperNoun {ProperNoun.sem} • NP  MassNoun {MassNoun.sem} • Assumption: meaning reps of children are passed up to parents for non-branching constuents • Verbs here are where the action is

  8. V  serves {E(e,x,y) Isa(e,Serving) ^ Server(e,x) ^ Served(e,y)} • Will every verb have its own distinct representation? • Predicate(Agent,Patient)… • How do we combine these pieces? • VP  V NP • Goal: E(e,x) Isa(e,Serving) ^ Server(e,x) ^ Served(e,Meat) • VP semantics must tell us • Which vars to be replaced by which args? • How this replacement is done?

  9. Lambda Notation • Extension to FOPC • x P(x) • + variable(s) + FOPC expression in those variables • Lambda binding • Apply lambda-expression to logical terms to bind lambda-expression’s parameters to terms (lambda reduction) • Simple process: substitute terms for variables in lambda expression xP(x)(car) P(car)

  10. Lambda notation provides requisite verb semantics • Formal parameter list makes variables within the body of the logical expression available for binding to external arguments provided by e.g. NPs • Lambda reduction implements the replacement • Semantic attachment for • V  serves {V.sem(NP.sem)} {E(e,x,y) Isa(e,Serving) ^ Server(e,y) ^ Served(e,x)} becomes {x E(e,y) Isa(e,Serving) ^ Server(e,y) ^ Served(e,x)} • Now ‘x’ is available to be bound when V.sem is applied to NP.sem

  11. -application binds x to value of NP.sem (Meat) • -reduction replaces x within -expression to Meat • Value of VP.sem becomes: {E(e,y) Isa(e,Serving) ^ Server(e,y) ^ Served(e,Meat)} • Similarly, we need a semantic attachment for S NP VP {VP.sem(NP.sem)} to add the subject NP to our semantic representation of AyCaramba serves meat • We need another -expression in the value of VP.sem • But currently V.sem doesn’t give us one • So, we change V.sem to include another -expression • V  serves {x y E(e) Isa(e,Serving) ^ Server(e,y) ^ Served(e,x)}

  12. VP semantics (V.sem(NP.sem) binds the outer -expression to the object NP (Meat) but leaves the inner -expression for subsequent binding to the subject NP when the semantics of S is determined {E(e) Isa(e,Serving) ^ Server(e,AyCaramba) ^ Served(e,Meat)}

  13. Some Additional Problems to Solve • Complex terms A restaurant serves meat. • ‘a restaurant’: E x Isa(x,Restaurant) • E e Isa(e,Serving) ^ Server(e,<E x Isa(x,Restaurant)>) ^ Served(e,Meat) • Allows quantified expressions to appear where terms can by providing rules to turn them into well-formed FOPC expressions • Quantifier scope Every restaurant serves meat. Every restaurant serves every meat.

  14. Appropriate representations for other constituents? • Adjective phrases: intersective semantics Nom  Adj Nom {x Nom.sem(x) ^ Isa(x,Adj.sem)} Adj  tiny x Isa(x, Restaurant) ^ Isa(x,Cheap) But….fake gun? Ex Isa(x, Gun) ^ AM(x,Fake)

  15. Doing Compositional Semantics • To incorporate semantics into grammar we must • Figure out right representation for a single constituent based on the parts of that constituent (e.g. Adj) • Figuring out the right representation for a category of constituents based on other grammar rules making use of that constituent (e.g Nom Adj Nom) • This gives us a set of function-like semantic attachments incorporated into our CFG • E.g. Nom  Adj Nom {x Nom.sem(x) ^ Isa(x,Adj.sem)}

  16. What do we do with them? • As we did with feature structures: • Alter an Early-style parser so when constituents (dot at the end of the rule) are completed, the attached semantic function applied and meaning representation created and stored with state • Or, let parser run to completion and then walk through resulting tree running semantic attachments from bottom-up

  17. Option 1 (Integrated Semantic Analysis) S  NP VP {VP.sem(NP.sem)} • VP.sem has been stored in state representing VP • NP.sem stored with the state for NP • When rule completed, go get value of VP.sem, go get NP.sem, and apply VP.sem to NP.sem • Store result in S.sem. • As fragments of input parsed, semantic fragments created • Can be used to block ambiguous representations

  18. Drawback • You also perform semantic analysis on orphaned constituents that play no role in final parse • Hence, case for pipelined approach: Do semantics after syntactic parse

  19. Non-Compositional Language • What do we do with language whose meaning isn’t derived from the meanings of its parts • Metaphor: You’re the cream in my coffee. • She’s the cream in George’s coffee. • The break-in was just the tip of the iceberg. • This was only the tip of Shirley’s iceberg. • Idioms: The old man finally kicked the bucket. • The old man finally kicked the proverbial bucket. • Solutions? • Mix lexical items with special grammar rules?

  20. Summing Up • Hypothesis: Principle of Compositionality • Semantics of NL sentences and phrases can be composed from the semantics of their subparts • Rules can be derived which map syntactic analysis to semantic representation (Rule-to-Rule Hypothesis) • Lambda notation provides a way to extend FOPC to this end • But coming up with rule2rule mappings is hard • Idioms, metaphors perplex the process

More Related