Determining the (co)referents of anaphors can be approached in a variety of ways, as in the case of semantic disambiguation. Linguistic and psycholinguistic principles that have been proposed include gender and number agreement of coreferential terms, C-command principles (e.g., an anaphor must be a pronoun if its referent is a sibling of one of its ancestors in the parse tree), (non)reflexive constraints (e.g., the subject and object cannot be coreferential in a simple clause such as  “John blamed him for the accident”), recency/salience (more recent/salient referents are preferred), and centering (the most likely term to be pronominalized in an utterance is the “center of attention”). An early heuristic algorithm that employed several features of this type to interpret anaphors was that of Hobbs (1979).  But selectional preferences are important as well. For example, in the sentence “He bumped against the sauce boat containing the raspberry syrup, spilling it,” the pronoun can be determined to be coreferential with the raspberry syrup rather than the sauce boat because spill prefers a liquid (or loose aggregate) as its object. With the alternative continuation, “… knocking it over,” the choice of coreferent would be reversed, because knock over prefers something solid and upright as its object. More subtle world knowledge may be involved as well, as in Terry Winograd's well-known example, “The city council refused the women a parade permit because they feared/advocated violence,” where they may refer to the city council or the women, depending on the choice of verb and the corresponding stereotypes that are evoked. Another complication concerns reference to collections of entities, related entities (such as parts), propositions, and events that can become referents of pronouns (such as they, this, and that) or of definite NPs (such as this situation or the door (of the house)) without having appeared explicitly as a noun phrase. Like other sorts of ambiguity, coreference ambiguity has been tackled with statistical techniques. These typically take into account factors like those mentioned, along with additional features such as antecedent animacy and prior frequency of occurrence, and use these as probabilistic evidence in making a choice of antecedent (e.g., Haghighi &amp; Klein 2010). Parameters of the model are learned from a corpus annotated with coreference relations and the requisite syntactic analyses.