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
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.
,,
,
),()(Re foodxServexstaurantx
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:
),(),(),(Pr
),(),(),(,,
lunchvEatenMaryvEatervuopBelieved
JohnuBelieverEatingvIsAbelievinguIsAvu
)),(,( lunchMaryEatingJohnBelieving
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
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
ressionexp
)()))((( APAxxP
• Sentence: AyCaramba serves meat• Goal:
• Augmented rules:
),(),(),( MeateServedAyCarambaeServerservingeeIsA
}{
}{
}.{
)}.(.{
)}.(.{
)},(),(),({
meatmeatN
AyCarambaAyCarambaN
semNNNP
semNPsemVPVPNPS
semNPsemVNPVVP
xeServedyeServerservingeIsAeyxservesV
ressionexp
Quantifiers
• Sentence: A restaurant serves meat• Goal:
• Augmented rules:
),(),(
),()Re,(
MeateServedxeServer
ServingeIsAstaurantxIsAxe
),(
)}('..{'
}{
)}.,('
}{
restaurantxIsAxrestauranta
xsemNxsemDetNDetNP
restaurantrestaurantN
semNxIsAxNN
aDet
Complex terms
• Current formula:
• Goal:
• What is needed:
),(),(
),()Re,(
MeateServedxeServer
ServingeIsAstaurantxIsAxe
),())Re,(,(
),(
MeateServedstaurantxxIsAeServer
ServingeIsAe
,...))(...,(,...))(...,(
,...))(...,(,...))(...,(
xPbodyxbodyxP
xPbodyxbodyxP
Quantifier scoping
• Sentence: Every restaurant has a menu• Formula with complex terms
• Reading 1:
• Reading 2:
)),(,(
))Re,(,(),(
menuyIsAyeHad
staurantxIsAxeHaverHavingeIsAe
),(),(),(),(
)Re,(
yeHadxeHaverHavingeIsAmenuyIsAye
staurantxIsAx
)),(),(),((
)Re,(),(
yeHadxeHaverHavingeIsAe
staurantxIsAxmenuyIsAy
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
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
• 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
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 1997.
• http://www.icsi.berkeley.edu/~framenet• 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 2005.
• Book: “FrameNet: Theory and Practice” (printed June 2005)
Proposition Bank (PropBank)
• Developed by Palmer and Marcus at UPenn.
• http://www.cis.upenn.edu/~ace
• 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.
Arg0 Arg1 Arg2 Arg3
Buy buyer thing bought seller price paid
Sell seller thing bought buyer price paid
Pay buyer price paid seller thing 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
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