Generating Referring Quantified Expressions

In this paper, we describe how quantifiers can be generated in a text generation system. By taking advantage of discourse and ontological information, quantified expressions can replace entities in a text, making the text more fluent and concise. In addition to avoiding ambiguities between distributive and collective readings in universal quantification generation, we will also show how different scope orderings between universal and existential quantitiers will result in different quantified expressions in our algorithm. 1 I n t r o d u c t i o n To convey information concisely and fluently, text generation systems often perform opportunistic text planning (Robin, 1995; Mellish et al., 1998) and employ advanced linguistic constructions such as ellipsis (Shaw, 1998). But a system can also take advantage of quantification and ontological information to generate concise references to entities at the discourse level. For example, a sentence such as "'The patient has an infusion line in each arm." is a more concise version of "The patient has an infusion line ir~ his left arm. The patient has an infusion line in his right arm." Quantification is an active research topic in logic, language, and philosophy(Carpenter, 1997; de Swart. 1998). Since natural language understanding systems need to obtain as few interpretations as possible from text, researchers have studied quantifier scope ambiguity extensively (Woods~ 1978;-Grosz et al., 1987; Hobbs and Shieber, 1987: Pereira, 1990; Moran and Pereira, 1992: Park, 1995). Research in quantification interpretation first transforms a sentence into predicate logic, raises tim quantifiers to the sentential level, and permutes these quantifiers {o obtain as many readings as possible relaled to quantifier scoping. Then, invalid readings are eliminated using various consl raints. Ambiguity in quantified expressions is caused by two main culprits. T h e first type of ambiguity involves the distributive reading versus the collective reading. In universal quantification, a referring ex100 pression refers to multiple entities. There is a potential ambiguity between whether the aggregated entities acted individually (distributive) or acted together as one (collective). Under the distributive reading, the sentence "All the nurses inspected the patient." implies that each nurse individually inspected the patient. Under the collective reading, the nurses inspected the patient together as a group. The other ambiguity in quantification involves multiple quantifiers in the same sentence. The sentence "A nurse inspected each patient." has two possible quantifier scope orderings. In Vpatient3nurse, the universal quantifier V has wide scope, outscoping the existential quantifier 3. This ordering means that each patient is inspected by a nurse, who might not be the same in each case. In the other scope order, 3nurseVpatient, a single, particular nurse inspected every patient. In both types of ambiguities, a generation system should make the desired reading clear. Fortunately, the difficulties of quantifier scope disambiguation faced by the understanding conmmnity do not apply to text generation. For generation, the problem is the reverse: given an unambiguous representation of a set of facts as input, how can it generate a quantified sentence that unambiguously conveys the intended meaning? In this paper, we propose an algorithm which selects an appropriate quantified expression to refer .to a set of entities using discourse and ontological knowledge. The algor i thm first identifies the entities for quantification in ;the input :propositions. Then anappropriate concept in the ontology is selected to refer to these entities. Using discourse and ontological information, the system determines if quantification is appropriate and if it is, which particular quantifier to use to minimize the anabiguity between distributive and collective readings. More importantly, when there are multiple quantifiers hi the same sentence, the algorithm generates different expressions for differen~ scope orderings. In this work, we focus on generating referring quantified expressions for entities which have been mentioned before in the discourse or can be inferred from an ontology. There are quantified expressions that do not refer to particular entities in a domain or discourse, such as generics (i.e. "All whales are mammals."), or negatives (i.e., "The patient has no allergies."). The synthesis of such quantifiers is currently performed in earlier stages.of the ((TYPE EVENT) (PRED ((PRED receive) (ID idl))) (ARGi ((PRED patient) (ID ptl))) (ARG2 ((PRED aprotinin) (ID apl))) generation process. (MODS ((PRED after) (ID id2) . In the next section;we..vdll..~orapaxe ou_r~.approach . . . . . . : . . . tTYRE_TIME).. ............... with previous work in the generation of quantified expressions. In Section 3, we will describe the application where the need for concise output motivated ) ) ) our research in quantification. The algorithm for generating universal quantifiers is detailed in Section 4, including how the system handles ambiguity between distributive and collective readings. Section 5 describes how our algorithm generates sentences with multiple quantifiers.