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Representing Meaning

Representing Meaning. Lecture 19 13 Sep 2007. 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

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Representing Meaning

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  1. Representing Meaning Lecture 19 13 Sep 2007

  2. 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 • Compositional Semantics • Syntax-driven methods of assigning semantics to sentences

  3. Semantic Processing • We’re going to discuss 2 ways to attack this problem (just as we did with parsing) • There’s the theoretically motivated correct and complete approach… • Computational/Compositional SemanticsCreate a FOL representation that accounts for all the entities, roles and relations present in a sentence. • And there are practical approaches that have some hope of being useful and successful. • Information extractionDo a superficial analysis that pulls out only the entities, relations and roles that are of interest to the consuming application.

  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?

  5. $ x , y Giving ( x )^ Giver ( John , x )^ Given ( y , x ) ^ Givee ( Mary , x )^ Isa ( y , Book ) Better • Turns out this representation isn’t quite as useful as it could be. • Giving(John, Mary, Book) • Better would be one where the “roles” or “cases” are separated out. E.g., consider: • Note: essentially Giver=Agent, Given=Theme, Givee=To-Poss

  6. 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

  7. Advantages • Can have variable number of arguments associated with an event: events have many roles and fillers can be glued on as appear in the input. • Specifies categories (e.g., book) so that we can make assertions about categories themselves as well as their instances. E.g., Isa(MobyDick, Novel), AKO(Novel, Book). • Reifies events so that they can be quantified and related to other events and objects via sets of defined relations. • Can see logical connections between closely related examples without the need for meaning postulates.

  8. Example • AyCaramba serves meat

  9. Compositional Analysis

  10. 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.

  11. Easy parts… NP -> PropNoun NP -> MassNoun PropNoun -> AyCaramba MassMoun -> meat Attachments {PropNoun.sem} {MassNoun.sem} {AyCaramba} {MEAT} Example

  12. S -> NP VP VP -> Verb NP Verb -> serves {VP.sem(NP.sem)} {Verb.sem(NP.sem) ??? Example

  13. Lambda Forms • A simple addition to FOPC • 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

  14. Example

  15. Example

  16. Example

  17. Example

  18. Syntax/Semantics Interface: Two Philosophies • Let the syntax do what syntax does well and don’t expect it to know much about meaning • In this approach, the lexical entry’s semantic attachments do all the work • Assume the syntax does know something about meaning • Here the grammar gets complicated and the lexicon simpler (constructional approach)

  19. Example • Mary freebled John the nim. • Who has it? • Where did he get it from? • Why?

  20. Example • Consider the attachments for the VPs VP -> Verb NP NP rule (gave Mary a book) VP -> Verb NP PP (gave a book to Mary) Assume the meaning representations should be the same for both. Under the lexicon-heavy scheme, the VP attachments are: VP.Sem (NP.Sem, NP.Sem) VP.Sem (NP.Sem, PP.Sem)

  21. Example • Under a syntax-heavy scheme we might want to do something like • VP -> V NP NP V.sem ^ Recip(NP1.sem) ^ Object(NP2.sem) • VP -> V NP PP V.Sem ^ Recip(PP.Sem) ^ Object(NP1.sem) • i.e the verb only contributes the predicate, the grammar “knows” the roles.

  22. 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

  23. Semantic Augmentation to CFG Rules • CFG Rules are attached with semantic attachments. • These semantic attachments specify how to compute the meaning representation of a construction from the meanings of its constituent parts. • A CFG rule with semantic attachment will be as follows: A  1,…,n { f(j.sem,…,k.sem) } • The meaning representation of A, A.sem, will be calculated by applying the function f to the semantic representations of some constituents.

  24. Naïve Approach ProperNoun  Anarkali {Anarkali } MassNoun  meat { Meat } NP  ProperNoun {ProperNoun.sem } NP  MassNoun { MassNoun.sem } Verb  serves {e,x,y ISA(e,Serving)  Server(e,x)  Served(e,y) } • But we cannot propagate this representation to upper levels.

  25. Using Lambda Notations ProperNoun  Anarkali { Anarkali } MassNoun  meat { Meat } NP  ProperNoun {ProperNoun.sem } NP  MassNoun { MassNoun.sem } Verb  serves {xy e ISA(e,Serving)  Server(e,y)  Served(e,x) } VP  Verb NP { Verb.sem(NP.sem) } S  NP VP { VP.sem(NP.sem) } application of lambda expressionlambda expression

  26. Quasi-Logical Form • During semantic analysis, we may use quantified expressions as terms. In this case, our formula will not be a FOPC formula. We call this form of formulas as quasi-logical form. • A quasi-logical form should be converted into a normal FOPC formula by applying simple syntactic translations. Server(e,<x ISA(x,Restaurant)>) a quasi-logical formula  x ISA(x,Restaurant )  Server(e,x) a normal FOPC formula

  27. S write(bertrand,principia) VP y.write(y,principia) NP bertand bertrand V x.y.write(y,x) NP principia writes principia Parse Tree with Logical Forms

  28. Pros and Cons • If you integrate semantic analysis into the parser as it is running… • You can use semantic constraints to cut off parses that make no sense • But you assign meaning representations to constituents that don’t take part in the correct (most probable) parse

  29. Complex Terms • Allow the compositional system to pass around representations like the following as objects with parts: Complex-Term →<Quantifier var body>

  30. Example • Our restaurant example winds up looking like • Big improvement…

  31. Conversion • So… complex terms wind up being embedded inside predicates. So pull them out and redistribute the parts in the right way… P(<quantifier, var, body>) turns into Quantifier var body connective P(var)

  32. Example

  33. Quantifiers and Connectives • If the quantifier is an existential, then the connective is an ^ (and) • If the quantifier is a universal, then the connective is an ->(implies)

  34. Multiple Complex Terms • Note that the conversion technique pulls the quantifiers out to the front of the logical form… • That leads to ambiguity if there’s more than one complex term in a sentence.

  35. Quantifier Ambiguity • Consider • Every restaurant has a menu • That could mean that every restaurant has a menu • Or that There’s some uber-menu out there and all restaurants have that menu

  36. Quantifier Scope Ambiguity

  37. Ambiguity • This turns out to be a lot like the prepositional phrase attachment problem • The number of possible interpretations goes up exponentially with the number of complex terms in the sentence • The best we can do is to come up with weak methods to prefer one interpretation over another

  38. Non-Compositionality • Unfortunately, there are lots of examples where the meaning (loosely defined) can’t be derived from the meanings of the parts • Idioms, jokes, irony, sarcasm, metaphor, metonymy, indirect requests, etc

  39. English Idioms • Kick the bucket, buy the farm, bite the bullet, run the show, bury the hatchet, etc… • Lots of these… constructions where the meaning of the whole is either • Totally unrelated to the meanings of the parts (kick the bucket) • Related in some opaque way (run the show)

  40. The Tip of the Iceberg • Describe this construction • A fixed phrase with a particular meaning • A syntactically and lexically flexible phrase with a particular meaning • A syntactically and lexically flexible phrase with a partially compositional meaning • …

  41. Example • Enron is the tip of the iceberg. NP -> “the tip of the iceberg” • Not so good… attested examples… • the tip of Mrs. Ford’s iceberg • the tip of a 1000-page iceberg • the merest tip of the iceberg • How about • That’s just the iceberg’s tip.

  42. Example • What we seem to need is something like • NP -> An initial NP with tip as its head followed by a subsequent PP with of as its head and that has iceberg as the head of its NP And that allows modifiers like merest, Mrs. Ford, and 1000-page to modify the relevant semantic forms

  43. Quantified Phrases • Consider A restaurant serves meat. • Assume that A restaurant looks like • If we do the normal lambda thing we get

  44. Semantic analysis • Goal: to form the formal structures from smaller pieces • Three approaches: • Syntax-driven semantic analysis • Semantic grammar • Information extraction: filling templates

  45. Semantic grammar • Syntactic parse trees only contain parts that are unimportant in semantic processing. • Ex: Mary wants to go to eat some Italian food • Rules in a semantic grammar • InfoRequest USER want to go to eat FOODTYPE • FOODTYPENATIONALITY FOODTYPE • NATIONALITYItalian/Mexican/….

  46. Semantic grammar (cont) Pros: • No need for syntactic parsing • Focus on relevant info • Semantic grammar helps to disambiguate Cons: • The grammar is domain-specific.

  47. Information extraction • The desired knowledge can be described by a relatively simple and fixed template. • Only a small part of the info in the text is relevant for filling the template. • No full parsing is needed: chunking, NE tagging, pattern matching, … • IE is a big field: e.g., MUC. KnowItAll

  48. Summary of semantic analysis • Goal: to form the formal structures from smaller pieces • Three approaches: • Syntax-driven semantic analysis • Semantic grammar • Information extraction

  49. Lexical Semantics

  50. Meaning • Traditionally, meaning in language has been studied from three perspectives • The meaning of a text or discourse • The meanings of individual sentences or utterances • The meanings of individual words • We started in the middle, now we’ll look at the meanings of individual words.

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