Semantic Roles and Disambiguation

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• Semantic Roles and Disambiguation

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Today

• Semantic Networks Wordnet
• Thematic Roles
• Selectional Restrictions
• Selectional Association
• Conceptual Dependency

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Semantic Networks

• Used to represent lexical relationships
• e.g. WordNet (George Miller et al)
• Most widely used hierarchically organized lexical
  database for English
• Synset set of synonyms, a dictionary-style
  definition (or gloss), and some examples of uses
  --gt a concept
• Databases for nouns, verbs, and modifiers
• Applications can traverse network to find
  synonyms, antonyms, hierarchies,...
• Available for download or online use
• http//www.cogsci.princeton.edu/wn

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Using WN, e.g. in Question-Answering

• Pasca Harabagiu 01 results on TREC corpus
• Parses questions to determine question type, key
  words (Who invented the light bulb?)
• Person question invent, light, bulb
• The modern world is an electrified world. It
  might be argued that any of a number of
  electrical appliances deserves a place on a list
  of the millennium's most significant inventions.
  The light bulb, in particular, profoundly changed
  human existence by illuminating the night and
  making it hospitable to a wide range of human
  activity. The electric light, one of the everyday
  conveniences that most affects our lives, was
  invented in 1879 simultaneously by Thomas Alva
  Edison in the United States and Sir Joseph Wilson
  Swan in England.
• Finding key words is not enough

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• Compare expected answer type to potential
  answers
• For questions of type person, expect answer is
  person
• Identify potential person names (NEs) in passages
  retrieved by IR
• Check in WN to find which of these are hyponyms
  of instance? Person?
• Or, Consider reformulations of question Who
  invented the light bulb
• For key words in query, look for WN synonyms
• E.g. Who fabricated the light bulb?
• Use this query for initial IR
• Results improve system accuracy by 147 (on some
  question types)

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Thematic Roles

• E w,x,y,z Giving(x) Giver(w,x) Givee(z, x)
  Given(y,x)
• A set of roles for each event
• Agent volitional causer -- John hit Bill.
• Experiencer experiencer of event Bill got a
  headache.
• Force non-volitional causer The concrete block
  struck Bill on the head.
• Theme/patient most affected participant John
  hit Bill.
• Result end product Bill got a headache.
• Content proposition of propositional event
  Bill thought he should take up martial arts.

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• Instrument instrument used -- John hit Bill
  with a bat
• Beneficiary qui bono John hit Bill to avenge
  his friend
• Source origin of object of transfer event Bill
  fled from New York to Timbuktu
• Goal destination of object -- Bill led from New
  York to Timbuktu
• But there are a lot of verbs, with a lot of
  frames
• Framenet encodes frames for many verb categories

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Thematic Roles and Selectional Restrictions

• Selectional restrictions semantic constraint
  that a word (lexeme) imposes on the concepts that
  go with it
• George hit Bill with
• .John/a gun/gusto.
• Jim killed his philodendron/a fly/Bill.
• ?His philodenron killed Jim.
• The flu/Misery killed Jim.

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• In practical use
• Given e.g. a verb and a corpus (plus FrameNet)
• What conceptual roles are likely to accompany it?
• What lexemes are likely to fill those roles?
• Assassinate
• Give
• Imagine
• Fall
• Serve

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Disambiguation via Selectional Restrictions

• Verbs are known by the company they keep
• Different verbs select for different thematic
  roles
• wash the dishes (takes washable-thing as patient)
• serve delicious dishes (takes food-type as
  patient)
• Method another semantic attachment in grammar
• Semantic attachment rules are applied as
  sentences are syntactically parsed
• VP --gt V NP
• V? serve ltthemegt themefood-type
• Selectional restriction violation no parse

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• But this means we must
• Write selectional restrictions for each sense of
  each predicate or use FrameNet
• Serve alone has 15 verb senses
• Hierarchical type information about each argument
  (using WordNet)
• How many hypernyms does dish have?
• How many lexemes are hyponyms of dish?
• But also
• Sometimes selectional restrictions dont restrict
  enough (Which dishes do you like?)
• Sometimes they restrict too much (Eat dirt, worm!
  Ill eat my hat!)
• Can we take a statistical approach?

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Selectional Association (Resnik 97)

• Selectional Preference Strength how much does a
  predicate tell us about the word class of its
  argument?
• George is a monster, George cooked a steak
• SR(v) How different is p(c), the probability
  that any direct object will be a member of some
  class c, from p(cv), the probability that a
  direct object of a specific verb will fall into
  that class?
• Estimate conditional probabilities of word senses
  from a parsed corpus, counting how often each
  predicate occurs with an object argument
• e.g. How likely is dish to be an object of
  served?
• Jane served/V the dish/Obj
• Then estimate the strength of association between
  each predicate and the super-class (hypernym) of
  the argument in Wordnet

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• E.g. For each object of serve (e.g. ragout, Mary,
  dish)
• Look up all its hypernym classes in WordNet (e.g
  dish isa piece of crockery, dish isa food item,)
• Distribute credit for dish (with serve) among
  all hypernym classes (sense) to which dish
  belongs (1/n for n classes)
• Pr(cv) is estimated at count(c,v)/count(v)
• Why does this work?
• Ambiguous words have many superordinate classes
• John served food/the dish/tuna/curry
• There is a common sense among these which gets
  credit in each instance, eventually dominating
  the likelihood score

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• How can we use this in wsd?
• Choose the class (sense) of the direct object
  with the highest probability, given the verb
• Mary served the dish proudly.
• Results
• Baselines
• random choice of word sense is 26.8
• choose most frequent sense (NB requires
  sense-labeled training corpus) is 58.2
• Resniks 44 correct with only pred/arg
  relations labeled

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Schank's Conceptual Dependency

• Eleven predicate primitives represent all
  predicates
• Atrans abstract transfer of possession or ctrl
  from x to y
• Ptrans physical transfer of object from one
  place to another
• Mtrans transfer of mental concepts
• Mbuild creation of new information w/in entity
• Propel, Move, Ingest, Expel, Speak, Attend

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• Objects decomposed into primitive categories and
  modifiers
• But few predicates result in very complex
  representations of simple things
• Ex,y Atrans(x) Actor(x,John) Object(x,Book)
  To(x,Mary) Ptrans(y) Actor(y,John)
  Object(y,Book) To(y,Mary)
• John gave Mary a Book

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Next time

• Chapter 155-6
• Homework III (the last, now) assigned