A Hybrid System For Quantifier Scoping

1. Introduction A prominent source of ambiguity confronting natural language processing systems is ambiguity of quantifier scope relations. For example, the sentence Some target was hit by every arrow has one reading on which the quantified noun phrase (NP) some target has wider scope than the quantified NP every arrow (some particular target got hit by all the arrows), and another reading on which every arrow has wide scope (each arrow hit some target or other). Many factors influence preferred scope readings. Semantic factors, for example: in Sam served one beer to all customers, we prefer wide scope for all because the alternative reading entails the unlikely scenario of patrons huddled around a single beer mug. Syntactic factors: e.g. embedded prepositional objects often scope over heads, as in Every teacher at some high school joined the union, whereas heads usually assume scope over NPs contained in a relative clause, as in Every teacher who is at some high school joined the union. Lexical factors (i.e. the lexical identity of quantifiers): e.g. each tends toward wide scope and a toward narrow scope. Linear order is a factor-leftmost quantifiers tend to have wide scope-and there are others as well. Given the relevance of different factors, a question arises: how can a system determine a scope reading based on the combination of factors present in any given sentence? The standard approach has two parts: f'wst, assign measures to the scoping influences of specific factors taken individually, and second, integrate the individual measures. The first task is performed by various "specialists". A system may have a lexical specialist which represents the wide scope tendency of each, a specialist which represents the inverse scoping tendency of an embedded prepositional object, a specialist which represents the tendency of quantifiers to scope according to linear order, and so on. The system will prefer those scope orders for which fint(fspecl, fspec 2 ....) is optimal, where lint is