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 • FOODTYPENATIONALITY FOODTYPE • NATIONALITYItalian/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, unrelatedmeanings • 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 controlledvocabulary 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 • http://www.cogsci.princeton.edu/~wn
WordNet (cont) • Nouns: • Hypernym: meal, lunch • Has-Member: crew, pilot • Has-part: table, leg • Antonym: leader, follower • Verbs: • Hypernym: travel, fly • Entail: snoresleep • 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