Semantics

1
Semantics

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

2
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

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Meaning representation
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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

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Requirements

• Verifiability
• Unambiguous representations
• Canonical form
• Inference
• Expressiveness

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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?

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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

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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?

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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?

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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

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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

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FOPC

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

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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.

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Elements of FOPC (cont)

• Logical connectives
• Quantifier
• Example All restaurants serve food.

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Inference rules

• Modus ponens
• Conjunction
• Disjunction
• Simplification
• .

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FOPC

• Elements of FOPC
• Representing
• Categories
• Events
• Time
• Aspect
• Belief

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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

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Representing time (cont)

• Reichenbachs approach
• E the time of the event
• U the time of the utterance
• R the reference point
• Example
• Past perfect I had arrived E gt R gt U
• Simple past I arrived ER gt U
• Present perfect I have arrived E gt RU

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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.

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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.

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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

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Representing beliefs

• John believes that Mary ate lunch.
• One possibility
• Another possibility

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Representing beliefs (cont)

• Substitution does not work
• Example
• John knows Flight 1045 is delayed
• Mary is on Flight 1045
• Does John know that Marys flight was delayed?
• ?FOPC is not sufficient.
• ?Use modal logic

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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

25
Outline

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

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Semantic analysis
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Semantic analysis

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

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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

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• Sentence AyCaramba serves meat
• Goal
• Augmented rules

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Quantifiers

• Sentence A restaurant serves meat
• Goal
• Augmented rules

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Complex terms

• Current formula
• Goal
• What is needed

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Quantifier scoping

• Sentence Every restaurant has a menu
• Formula with complex terms
• Reading 1
• Reading 2

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Semantic analysis

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

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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/.

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Semantic grammar (cont)

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

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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

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Summary of semantic analysis

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

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Outline

• Meaning representation
• Semantic analysis
• Lexical semantics

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Lexical semantics
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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

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Important resources

• Dictionaries
• Ontology and taxonomy
• WordNet
• FrameNet
• PropBank
• Levins English verb classes
• .

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Meaning of words

• Lexeme is an entry in the lexicon that includes
• Orthographic form
• Phonological form
• Sense lexemes meaning

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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

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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.
• Whats the difference between polysemy and
  homonymy
• Homonymy distinct, unrelated meanings
• Polysemy distinct but related meanings
• How to decide etymology, notion of coincidence

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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

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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.

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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.

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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

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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

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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

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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.

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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

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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.

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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.

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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

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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

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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,.

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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)

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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

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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

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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
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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

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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.

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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,

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Levins 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),.

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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

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Summary of lexical semantics (2)

• Predicate-argument structures
• Thematic roles
• Selectional restrictions
• FrameNet
• PropBank

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Summary of lexical semantics (3)

• Mapping from conceptual structures to grammatical
  functions
• Word classes and alternations
• Levins verb classes for English

69
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
• Levins verb classes