Lecture 21: Local Discourse Context

Coreference analysis means figuring out the meaning of pronouns in sentences like: "John saw him in the mirror" versus "John saw himself in the mirror."  That's anaphor resolution.  More generally, "John saw Bill and said 'My friend, you look lovely today.'" we need to figure out that My=John and friend=Bill.

Generally we care about three "mention" types:

• Pronouns (he, she, it, I, etc...)
• Proper nouns (John, Bill, Congress, the White House, etc...)
• Common nouns (the soccer player, the man, etc.)

Not all pronouns are coreference, as in pleonastic it: "It is raining" but most are.

There are lots of signals of coreference:

• Apposition: "John, mayor of Tinyville, is happy."  Here, it's pretty clear that John=mayor
  
• Syntactic structure with pronouns/-self.  "John saw him" versus "... himself"
  
• Gender / number / type agreement.  "He" is probably not "Mary" and "him" is probably not "players".  Similarly, "he" is probably not "the White House".  (Mr. and Mrs. are helpful here.)
  
• Locality.  "He" probably refers back to a nearby referent, not something far away.
  
• Discourse focus.  New entities are likely to transition from being objects to being subjects, not the other way around.
  
• Sub/super-string matching.  "Bill Clinton" and "Clinton" are likely coreferent.
  
• New entities are often introduced with indeterminant NPs ("a player") and then later with determinant NPs
  
• World knowledge: sometimes we just know that Obama is the President

In general, matches between different mention types work as follows:

• Named to named: very easy, 95% accuracy just using substring matching
• Named to pronoun: pretty easy, 85-90% using Hobbs' heuristics, locality and gender
• Named to common: very hard (often need world knowledge)
• Common to common: very hard (often need lexical semantics)
• Common to pronoun: don't bother -- do common to named and named to pronoun

Basic algorithms for coreference resolution treat it as a binary classification problem: are these two mentions coreferent or not.  (That is, after we've done mention detection ala named entity recognition.)  Some issues with this approach:

• You then need to go back and fix things up to make sure that transitivity is enforced; or use left-to-right search
  
• Number of -ve examples >> number of +ve examples; often subsample the negatives
  
• These decisions aren't independent

Some things that make life difficult:

• Metonymy: when we say "the White House" meaning "Obama"
  
• Common nouns that aren't mentions of an entity: "This life is wonderful"
  
• Quantified pronouns: "Every man loves his mother" (might or might not be coreferent)
  
• World knowledge

My general feeling about world knowledge is that if it's commonly known it might not be stated (eg., we won't always see "Obama, President of the US, ...") but if it's not commonly known it will be made really obvious (eg., "Joe Shmoe, soccer player for the team").  We can, however, mine this common knowledge using the sorts of techniques we talked about last week.