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					   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.
                    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
          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
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: 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 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
 Summary of lexical semantics (3)
• Mapping from conceptual structures to
  grammatical functions
• Word classes and alternations

• Levin’s verb classes for English
          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