Generating Referring Quantified Expressions James Shaw and Kathleen McKeown Dept. of Computer Science Columbia University , :, ~.*~ . New York, NY 10027, USA shaw, kathy*cs, columbia, edu Abstract pression refers to multiple entities. There is a po- In this paper, we describe how quantifiers can be tential ambiguity between whether the aggregated generated in a text generation system. By taking entities acted individually (distributive) or acted to- advantage of discourse and ontological information, gether as one (collective). Under the distributive quantified expressions can replace entities in a text, reading, the sentence "All the nurses inspected the making the text more fluent and concise. In ad- patient." implies that each nurse individually in- dition to avoiding ambiguities between distributive spected the patient. Under the collective reading, and collective readings in universal quantification the nurses inspected the patient together as a group. generation, we will also show how different scope The other ambiguity in quantification involves mul- orderings between universal and existential quanti- tiple quantifiers in the same sentence. The sentence tiers will result in different quantified expressions in "A nurse inspected each patient." has two possi- our algorithm. ble quantifier scope orderings. In Vpatient3nurse, the universal quantifier V has wide scope, outscop- 1 Introduction ing the existential quantifier 3. This ordering means that each patient is inspected by a nurse, who might To convey information concisely and fluently, text not be the same in each case. In the other scope generation systems often perform opportunistic text order, 3nurseVpatient, a single, particular nurse in- planning (Robin, 1995; Mellish et al., 1998) and em- spected every patient. In both types of ambiguities, ploy advanced linguistic constructions such as ellip- a generation system should make the desired reading sis (Shaw, 1998). But a system can also take ad- clear. vantage of quantification and ontological informa- tion to generate concise references to entities at the Fortunately, the difficulties of quantifier scope dis- discourse level. For example, a sentence such as ambiguation faced by the understanding conmmnity "'The patient has an infusion line in each arm." is do not apply to text generation. For generation, the a more concise version of "The patient has an in- problem is the reverse: given an unambiguous rep- fusion line ir~ his left arm. The patient has an in- resentation of a set of facts as input, how can it fusion line in his right arm." Quantification is an generate a quantified sentence that unambiguously active research topic in logic, language, and philoso- conveys the intended meaning? In this paper, we phy(Carpenter, 1997; de Swart. 1998). Since nat- propose an algorithm which selects an appropriate ural language understanding systems need to ob- quantified expression to refer .to a set of entities us- tain as few interpretations as possible from text, ing discourse and ontological knowledge. The algo- researchers have studied quantifier scope ambigu- rithm first identifies the entities for quantification in ity extensively (Woods~ 1978;-Grosz et al., 1987; ;the input :propositions. Then an- appropriate con- Hobbs and Shieber, 1987: Pereira, 1990; Moran and cept in the ontology is selected to refer to these en- Pereira, 1992: Park, 1995). Research in quantifica- tities. Using discourse and ontological information, tion interpretation first transforms a sentence into the system determines if quantification is appropri- predicate logic, raises tim quantifiers to the senten- ate and if it is, which particular quantifier to use tial level, and permutes these quantifiers {o obtain to minimize the anabiguity between distributive and as many readings as possible relaled to quantifier collective readings. More importantly, when there scoping. Then, invalid readings are eliminated using are multiple quantifiers hi the same sentence, the al- various consl raints. gorithm generates different expressions for differen~ Ambiguity in quantified expressions is caused by scope orderings. In this work, we focus on generat- two main culprits. The first type of ambiguity in- ing referring quantified expressions for entities which volves the distributive reading versus the collective have been mentioned before in the discourse or can reading. In universal quantification, a referring ex- be inferred from an ontology. There are quantified 100 expressions that do not refer to particular entities in a domain or discourse, such as generics (i.e. "All ((TYPE EVENT) whales are mammals."), or negatives (i.e., "The pa- (PRED ((PRED receive) (ID idl))) tient has no allergies."). The synthesis of such quan- (ARGi ((PRED patient) (ID ptl))) tifiers is currently performed in earlier stages.of the (ARG2 ((PRED aprotinin) (ID apl))) generation process. (MODS ((PRED after) (ID id2) . In the next section;we..vdll..~orapaxe ou_r~.approach ..... .:. .. tTYRE_TIME)................. f ....... with previous work in the generation of quantified (ARG2 ((PRED critical-point) expressions. In Section 3, we will describe the appli- (NAME intubation) (IDcl))) cation where the need for concise output motivated ))) our research in quantification. The algorithm for generating universal quantifiers is detailed in Sec- Figure h The predicate-argument structure of tion 4, including how the system handles ambiguity "After intubation, a patient received aprotinin." between distributive and collective readings. Sec- tion 5 describes how our algorithm generates sen- tences with multiple quantifiers. dard text generation system architecture with three 2 Related Work modules (Rambow and Korelsky, 1992): a content planner, a sentence planner, and a linguistic realizer. Because a quantified expression refers to multiple Once the bypass surgery is finished, information that entities in a domain, our work can be categorized as is automatically collected during surgery such as referring expression generation (Dale, 1992; Reiter blood pressure, heart rate, and medications given, and Dale, 1992; Horacek, 1997). Previous work in is sent to a domain=specific medical inference mod- this area did not address the generation of quantified ule. Based on the medical inferences and schemas expressions directly. In this paper, we are interested (McKeown, 1985), the content planner determines in how to systematically derive quantifiers from in- the information to convey and the order to convey put propositions, discourse history, and ontological it. information. Recent work on the generation ofquan- tifiers (Gailly, 1988; Creaney, 1996; Creaney, 1999) The sentence planner takes a set of propositions follows the analysis viewpoint, discussing scope ana- (or predicate-argument structures) with rhetorical biguities extensively. Though our algorithm gener- relations from the content planner and uses linguistic ates different sentences for different scope orderings, information to make decisions about how to convey we do not achieve this through scoping operations as the propositions fluently. Each proposition is repre- they did. Creaney also discussed various imprecise sented as a feature structure (Kaplan and Bresnan, quantifiers, such as some, at least, and at most. 1982; Kay, 1979) similar to the one shown in Fig- In regards to generating generic quantified expres- ure 1. The sentence planner's responsibilities include sions, (Knott et al., 1997) has proposed an algorithm referring expression generation, clause aggregation, for generating defeasible, but informative descrip- and lexical choice (Wanner and How, 1996). Then tions for objects in nmseums. the aggregated predicate-argument structure is sent Other researchers (van Eijck and Alshawi, 1992; to FUF/SURGE (Elhadad and Robin, 1992), a lin- Copestake et al., 1999) proposed representations in a guistic realizer which t.ransforms the lexicalized se- machine translation setting which allow underspec- inantic specification into a string. The quantification ification in regard to quantifier scope. Our work is algorithm is implemented in the sentence planner. different, in that we perform quantification directly on the instance-based representation obtained from 4 Quantification Algorithm database tuples. Our input .does not have the in-.. in this:,work, weprefergenerating expressions with formation about which entities are quantified as is universal quantifiers over conjunction because, as- the case in machine translation, where the quanti- suming that the users and the system have tile same tiers are already specified in the input from a source domain model, the universally quantified expres- language. sions are more concise and they represent the same amount of information as the expression with con- 3 The Application Domain joined entities. In contrast,, when given a conjunc- We implemented our quantification algorithm as tion of entities and an expression with a cardinal part of MAGIC (Dalai et al., 1996: McKeown et quantifier, the system, by default, would use the al., 1997). MAGIC automatically generates multi- conjunction if the conjoined entities can be distin- media briefings to describe the post-operative sta- guished at the surface level. This is because once tus of a patient after undergoing Coronary Artery the system generates a cardinal quantifier when the Bypass Graft, surgery. The system embodies a stan- universal quantification does not hold, such as "three 101 patients", it is impossible for the hearer to recover