CMSC 723 Intro to Computational Linguistics November 24 2004 Meaning Lecture 12 Lexical Semantics Bonnie Dorr Christof Monz So far we have focused on the structure of language not on what things mean We have seen that words have different meaning depending on the context in which they are used Every day language tasks that require some semantic processing Meaning continued meaning representations are representations that link linguistic forms to knowledge of the world We are going to cover What is the meaning of a word How can we represent the meaning What formalisms can be used Answering an essay question on an exam Deciding what to order at a restaurant by reading a menu Realizing you ve been insulted What Can Serve as a Meaning Representation Anything that serves the core practical purposes of a program that is doing semantic processing What is a Meaning Representation Language What is Semantic Analysis Meaning representation languages 1 Requirements for Meaning Representation Verifiability Verifiability Unambiguous Representation Canonical Form Inference Expressiveness Unambiguous Representation Ambiguity and Vagueness Single linguistic input can have different meaning representations Each representation unambiguously characterizes one meaning Example small cars and motorcycles are allowed car x small x motorcycle y small y allowed x allowed y car x small x motorcycle y allowed x allowed y System can match input representation against representations in knowledge base If it finds a match it can return Yes Otherwise No Does Maharani serve vegetarian food Serves Maharani vegetarian food An expression is ambiguous if in a given context it can be disambiguated to have a specific meaning from a number of discrete possible meanings E g bank financial institution vs bank river bank An expression is vague if it refers to a range of a scalar variable such that even in a specific context it s hard to specify the range entirely E g he s tall it s warm etc 2 Representing Similar Concepts Distinct inputs could have the same meaning Does Maharani have vegetarian dishes Do they have vegetarian food at Maharani Are vegetarian dishes served at Maharani Does Maharani serve vegetarian fare Four different semantic representations Store all possible meaning representations in KB How to Produce a Canonical Form Systematic Meaning Representations can be derived from thesaurus Solution Inputs that mean same thing have same meaning representation Is this easy No Vegetarian dishes vegetarian food vegetarian fare Have serve Alternatives Canonical Form food dish one overlapping meaning sense fare We can systematically relate syntactic constructions S NP Maharani serves NP vegetarian dishes S NP vegetarian dishes are served at NP Maharani What to do Inference Consider a more complex request Can vegetarians eat at Maharani Vs Does Maharani serve vegetarian food Why do these result in the same answer Inference Draw conclusions about truth of propositions not explicitly stored in KB serve Maharani VegetarianFood CanEat Vegetarians AtMaharani 3 Non Yes No Questions Meaning Structure of Language Example I d like to find a restaurant where I can get vegetarian food serve x VegetarianFood Matching succeeds only if variable x can be replaced by known object in KB Compositionality Predicate Argument Structure The compositionality principle is an important principle in formal semantics Display a basic predicate argument structure Make use of variables Make use of quantifiers Display a partially compositional semantics The meaning of an expression is a strict function of the meanings of its parts It allows to build meaning representations incrementally Standard predicate logic does not adhere to this principle donkey sentences Human Languages Represent concepts and relationships among them Some words act like arguments and some words act like predicates Nouns as concepts or arguments red ball Adj Adv Verbs as predicates red ball Subcategorization argument frames specify number position and syntactic category of arguments Examples NP give NP2 NP1 NP give NP1 to NP2 give x y z 4 Semantic thematic Roles Semantic Roles Participants in an event Agent George hit Bill Bill was hit by George Patient George hit Bill Bill was hit by George Semantic Selectional Restrictions Constrain the types of arguments verbs take George assassinated the senator The spider assassinated the fly Verb subcategorization Allows linking arguments in surface structure with their semantic roles Prepositions are like verbs Under ItalianRestaurant 15 FOPC Syntax Terms Constants Maharani Functions LocationOf Maharani Variables x in LocationOf x Predicates Relations that hold among objects Logical Connectives Permit compositionality of meaning First Order Predicate Calculus FOPC FOPC provides sound computational basis for verifiability inference expressiveness Supports determination of truth Supports compositionality of meaning Supports question answering via variables Supports inference FOPC Semantics Sentences in FOPC can be assigned truth values True or False Serves Maharani VegetarianFood I only have 5 and I don t have a lot of time Have I 5 Have I LotofTime 5 Variables and Quantifiers Existential There exists FOPC Examples A restaurant that serves Mexican food near UMD x Restaurant x Serves x MexicalFood Near LocationOf x LocationOf UMD John gave Mary a book Better Universal For all restaurants serve vegetarian All vegetarian food x VegetarianRestaurant x Serves x VegetarianFood w x y z Giving x Giver w x Givee z x Given y x Solution with Variables Multiple sentences containing eat x Giving x Giver John x Givee Mary x Given book x Full Definition of Give Why use Variables Previously Give John Mary book I ate I ate a turkey sandwich I ate a turkey sandwich at my desk I ate at my desk I ate lunch I ate a turkey sandwich for lunch I ate a turkey sandwich for lunch at my desk Seven different Representations Eating1 Speaker Eating2 Speaker TurkeySandwich Eating3 Speaker TurkeySandwich Desk Eating4 Speaker Desk Eating5 Speaker Lunch Eating6 Speaker TurkeySandwich Lunch Eating7 Speaker TurkeySandwich Lunch Desk Eating v w x y Examples revisited w x y Eating Speaker w x y x y Eating Speaker TurkeySandwich x y x Eating Speaker TurkeySandwich x Desk w x Eating Speaker w x Desk w y Eating Speaker w Lunch y y Eating Speaker TurkeySandwich Lunch y Eating Speaker TurkeySandwich Lunch Desk 6 Representing Time Events are associated with points or intervals in