Semantics Ling 571 Fei Xia Week 6: 11/1-11/3/05

Outline Meaning representation: what formal structures should be used to represent the meaning of a sentence? Semantic analysis: how to form the formal structures from smaller pieces? Lexical semantics:

Meaning representation

Requirements that meaning representations should fulfill Types of meaning representation: –First order predicate calculus (FOPC) –Frame-based representation –Semantic network –Conceptual dependency diagram

Requirements Verifiability Unambiguous representations Canonical form Inference Expressiveness

Verifiability A system's ability to compare the state of affairs described by a representation to the state of affairs in some world as modeled in a knowledge base Example: –Sent: Maharani serves vegetarian dishes. –Question: Is the statement true?

Unambiguous representation Representations should have a single unambiguous interpretation. Example: –Mary and John bought a book –Two students met three teachers –A German teacher –A Chinese restaurant –A Canadian restaurant

Canonical form Sentences with the same thing should have the same meaning representation Example: –Alternations: active/passive, dative shift –Does Maharani have vegetarian dishes? –Do they serve vegetarian food at Maharani?

Inference a system's ability to draw valid conclusions based on the meaning representation of inputs and its store of background knowledge. Example: –Sent: Maharani serves vegetarian dishes –Question: can vegetarians eat at Maharani?

Expressiveness A system should be expressive enough to handle an extremely wide range of subject matter. Example: –Belief: I think that he is smart. –Hypothetical statement: If I were you, I would buy that book. –Former president, fake ID, allegedly, apprarently

Meaning representation Requirements –Verifiability –Unambiguous representations –Canonical form –Inference –Expressiveness Types of meaning representation: –First order predicate calculus (FOPC) –Frame-based representation –Semantic network –Conceptual dependency diagram

FOPC Elements of FOPC Representing –Categories –Events –Time (including tense) –Aspect –Belief –…

Elements of FOPC Terms: –Constant: specific objects in the world: e.g., Maharani –Variable: a particular unknown object or an arbitrary object: e.g., a restaurant –Function: concepts: e.g., LocationOf(Maharani) Predicates: referring to relations that hold among objects: –Ex: Serve(Maharani, food) –Arguments of predicates must be terms.

Elements of FOPC (cont) Logical connectives: Quantifier: Example: All restaurants serve food.

Inference rules Modus ponens: Conjunction: Disjunction: Simplification: ….

FOPC Elements of FOPC Representing –Categories –Events –Time –Aspect –Belief –…

Representing time Past perfect: I had arrived in NY Simple past: I arrived in NY Present perfect: I have arrived in NY Present: I arrive in NY Simple future: I will arrive in NY Future perfect: I will have arrived in NY

Representing time (cont) Reichenbach’s approach –E: the time of the event –U: the time of the utterance –R: the reference point Example: –Past perfect: I had arrived: E > R > U –Simple past: I arrived: E=R > U –Present perfect: I have arrived: E > R=U

Aspect Four types of event expression: –Stative: I like books. I have a ticket –Activity: She drove a Mazda. I live in NY –Accomplishment: Sally booked her flight. –Achievement: He reached NY. Differences: –Being in a state or not –occurring at a given time, or over some span of a time –Resulting in a state: happening in an instant or not.

Distinguishing four types Allowing progressive, imperative –*I am liking books. –*Like books. Modified by in-phrase, for-phrase: in a month, for a mont –He lived in NY for five years. –*He reached NY for five minutes.

Distinguishing four types (cont) “Stop” test: stop doing something –*He stopped reaching NY. –He stopped booking the ticket Modified by adverbs such as “deliberately”, “carefully” –* He likes books deliberately

Representing beliefs John believes that Mary ate lunch. One possibility: Another possibility:

Representing beliefs (cont) Substitution does not work Example: –John knows Flight 1045 is delayed –Mary is on Flight 1045 –Does John know that Mary’s flight was delayed?  FOPC is not sufficient.  Use modal logic

Summary of meaning representation Five requirements: –Verifiability –Unambiguous representations –Canonical form –Inference –Expressiveness Four types of representations: –First order predicate calculus (FOPC) –Frame-based representation –Semantic network –Conceptual dependency diagram

Outline Meaning representation: Semantic analysis: how to form the formal structures from smaller pieces? Lexical semantics:

Semantic analysis

Goal: to form the formal structures from smaller pieces Three approaches: –Syntax-driven semantic analysis –Semantic grammars –Information extraction: filling templates

Syntax-driven approach Parsing then semantic analysis, or parsing with semantic analysis. Semantic augmentations to grammars (e.g., CFG or LTAG) –Associate FOPC expression with lexical items –Use –Use complex-terms

Sentence: AyCaramba serves meat Goal: Augmented rules:

Quantifiers Sentence: A restaurant serves meat Goal: Augmented rules:

Complex terms Current formula: Goal: What is needed:

Quantifier scoping Sentence: Every restaurant has a menu Formula with complex terms Reading 1: Reading 2:

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

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/….

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

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

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

Outline Meaning representation Semantic analysis Lexical semantics

What is lexical semantics? Meaning of word: word senses Relations among words: Predicate-argument structures Thematic roles Selectional restrictions Mapping from conceptual structures to grammatical functions Word classes and alternations

Important resources Dictionaries Ontology and taxonomy WordNet FrameNet PropBank Levin’s English verb classes ….

Meaning of words Lexeme is an entry in the lexicon that includes –Orthographic form –Phonological form –Sense: lexeme’s meaning

Relations among lexemes Homonyms: same orth. and phon. forms, but different, unrelated meanings –bank vs. bank Homophones: same phon. different orth –read vs. red, to, two, and too. Homographs: same orth, different phon. –bass vs. bass

Polysemy Word with multiple but related meanings –He served his time in prison –He served as U.N. ambassador –They rarely served lunch after 3pm. What’s the difference between polysemy and homonymy: –Homonymy: distinct, unrelated meanings –Polysemy: distinct but related meanings –How to decide: etymology, notion of coincidence

Synonymy Different lexemes with the same meaning Substitutable in some environment: –How big is that plane? –How large is that plane? What influences substitutablity? –Polysemy: big brother vs. large brother –Subtle shade of meaning: first class fare/?price –Colllocational constraints: big/?large mistake –Register: social factors

Hyponymy General: hypernym –“vehicle” is a hypernym of “car” Specific: hyponym –“car” is a hyponym of “vehicle”. Test: X is a car implies that X is a vehicle.

Ontology and taxonomy Ontology: –It is a specification of a conceptualization of a knowledge domain –It is a controlled vocabulary that describes objects and the relations between them in a formal way, and has strict rules about how to specify terms and relationships. Taxonomy: –A taxonomy is a hierarchical data structure or a type of classification schema made up of classes, where a child of a taxonomy node represents a more restricted, smaller, subclass than its parent. –a particular arrangement of the elements of an ontology into a tree-like class inclusion structure.

WordNet Most widely used lexical database for English Developed by George Miller etc. at Princeton Three databases: Noun, Verb, Adj/Adv Each entry in a database: a unique orthographic form + a set of senses Synset: a set of synonyms

WordNet (cont) Nouns: –Hypernym: meal, lunch –Has-Member: crew, pilot –Has-part: table, leg –Antonym: leader, follower Verbs: –Hypernym: travel, fly –Entail: snore  sleep –Antonym: increase  decrease Adj/Adv: –Antonym: heavy  light, quickly  slowly

Lexical semantics Meaning of word: word senses Relations among words: Predicate-argument structures Thematic roles Selectional restrictions Mapping from conceptual structures to grammatical functions Word classes and alternations

Predicate-argument structure Predicate-argument: –Verb/adj as predicate –Nouns etc. as arguments –Example: buy(Mary, book) Subcategorization frame: –specify number, position, and syntactic category of arguments (or complements) –Example: (NP, NP): I want Italian food (NP, Inf-VP): I want to save money (NP, NP, Inf-VP): I want the book to be delivered tomorrow.

Thematic (Semantic) roles A set of roles: –Agent: the volitional causer of an event –Force: the non-volitional causer of an event –Patient/Theme: the one most directly affected by an event –Experiencer: the experiencer of an event –Others: Instrument, Source, Goal, Beneficiary, … Example: –John broke a glass –John broke an ankle in the game

Selectional restriction Mary ate the cake ?The table ate the cake Mary ate Italian food with her friends. Mary ate somewhere with her friends. White house announced that … The spider assassinated the fly.

FrameNet Developed by Fillmore and Baker at UC Berkeley since FrameNet database has two parts: –Frame database: a list of semantic frames, and relations between them, such as frame inheritance and frame composition. –Lexical database: each entry (called a lexical unit) is a (lemma, semantic frame) pair.

Semantic frames Definition Frame elements (FEs): conceptual structure –Core FEs: Communicator, Medium, Message, Topic –Non-Core FEs: time, place, manner Inherit from: Subframes: Lexical units: Example sentences:

One frame Frame: Communication –Definition: A Communicator conveys a Message to an Addressee. the Topic and Medium of the communication also may be expressed. –Core FEs: Addressee, Communicator, Medium, Message, Topic –Lexical units: communicate, indicate, signal

Another frame Frame: Statement –Inherit from: Communication –Definition: This frame contains verbs and nouns that communicate the act of a Speaker to address a Message to some Addressee using language. –Core FEs: Communicator, Medium, Message, Topic –Lexical units: admit, affirm, express,….

Project status More than 625 semantic frames, 8900 entries in the lexicon. Version 1.2 released in June Book: “FrameNet: Theory and Practice” (printed June 2005)

Proposition Bank (PropBank) Developed by Palmer and Marcus at UPenn. Annotate the English Penn Treebank with predicate-argument information Corpus can be used for automatic labeling of thematic roles

Semantic tags Main tags: –Arg0: Agent –Arg1: theme or direct object –Arg2: instrument, indirect object –… Secondary tags: –ArgM-DIR: direction –ArgM-LOC: locative –ArgM-NEG: negation –ArgM-DIS: discourse –…

Semantic tags (cont) Main tags are defined based on each verb. Example: –Buy: John bought a book from Mary for 5 dollars –Sell: Mary sold a book to John for 5 dollars –Pay: John paid Mary 5 dollars for a book. Arg0Arg1Arg2Arg3 Buybuyerthing boughtsellerprice paid Sellsellerthing boughtbuyerprice paid Paybuyerprice paidsellerthing bought

Lexical semantics Meaning of word: word senses Relations among words: Predicate-argument structures Thematic roles Selectional restrictions Mapping from conceptual structure to grammatical function Word classes and alternations

Mapping between conceptual structure and grammatical function Buy: buyer, thing bought, seller, price,…. Possible syntactic realizations: –(buyer, thing bought): John bought a book –(price, thing bought): $5 can buy two books –(thing bought, seller): The book was bought from Mary –(buyer, thing bought, seller): John bought a book from Mary. –**(buyer, price): John bought $5.

Alternations An alternation is a set of different mappings of conceptual roles to grammatical function. Example: dative alternation –John gave Mary a book –John gave a book to Mary Verb classes: give, donate,

Levin’s verb classes Levin (1993): –Verb classes –Alternations –Show the list of alternatives a verb class can take. Problems: –Many verbs appear in multiple classes –Verbs in the same classes do not behave exactly the same: e.g, (meet, visit), (give, donate),….

Summary of lexical semantics (1) Meaning of word: word senses Relations among words: –Homonyms: bank, bank –Homophones: read. red –Homographs: bass, bass –Polysemy: bank: blood bank, financial bank –Synonyms: big, large –Hypernym/Hyponym: vehicle, car Ontology and taxonomy WordNet

Summary of lexical semantics (2) Predicate-argument structures Thematic roles Selectional restrictions FrameNet PropBank

Mapping from conceptual structures to grammatical functions Word classes and alternations Levin’s verb classes for English Summary of lexical semantics (3)

Summary of semantics Meaning representation: –Criteria for good representation –First-order predicate calculus (FOPC) Semantic analysis: –Syntax-based semantic analysis –Semantic grammar –Information extraction Lexical semantics: –WordNet –FrameNet –PropBank –Levin’s verb classes