Ordering Among Premodifiers

Ordering Among Premodifiers James Shaw and Vasileios Hatzivassiloglou Department of Computer Science Columbia University New York, N.Y. 10027, USA {shaw, vh}@cs, columbia, edu Abstract We present a corpus-based study of the se- quential ordering among premodifiers in noun phrases. This information is important for the fluency of generated text in practical appli- cations. We propose and evaluate three ap- proaches to identify sequential order among pre- modifiers: direct evidence, transitive closure, and clustering. Our implemented system can make over 94% of such ordering decisions cor- rectly, as evaluated on a large, previously un- seen test corpus. 1 Introduction Sequential ordering among premodifiers affects the fluency of text, e.g., "large foreign finan- cial firms" or "zero-coupon global bonds" are desirable, while "foreign large financial firms" or "global zero-coupon bonds" sound odd. The difficulties in specifying a consistent ordering of adjectives have already been noted by linguists [Whorf 1956; Vendler 1968]. During the process of generating complex sentences by combining multiple clauses, there are situations where mul- tiple adjectives or nouns modify the same head noun. The text generation system must order these modifiers in a similar way as domain ex- perts use them to ensure fluency of the text. For example, the description of the age of a patient precedes his ethnicity and gender in medical do- main as in % 50 year-old white female patient". Yet, general lexicons such as WordNet [Miller et al. 1990] and COMLEX [Grishman et al. 1994], do not store such information. In this paper, we present automated tech- niques for addressing this problem of determin- ing, given two premodifiers A and B, the pre- ferred ordering between them. Our methods rely on and generalize empirical evidence ob- tained from large corpora, and are evaluated objectively on such corpora. They are informed and motivated by our practical need for order- ing multiple premodifiers in the MAGIC system [Dalal et al. 1996]. MAGIC utilizes co-ordinated text, speech, and graphics to convey informa- tion about a patient's status after coronary by- pass surgery; it generates concise but complex descriptions that frequently involve four or more premodifiers in the same noun phrase. To demonstrate that a significant portion of noun phrases have multiple premodifiers, we extracted all the noun phrases (NPs, exclud- ing pronouns) in a two million word corpus of medical discharge summaries and a 1.5 million word Wall Street Journal (WSJ) corpus (see Section 4 for a more detailed description of the corpora). In the medical corpus, out of 612,718 NPs, 12% have multiple premodifiers and 6% contain solely multiple adjectival premodifiers. In the WSJ corpus, the percentages are a little lower, 8% and 2%, respectively. These percent- ages imply that one in ten NPs contains mul- tiple premodifiers while one in 25 contains just multiple adjectives. Traditionally, linguists study the premodifier ordering problem using a class-based approach. Based on a corpus, they propose various se- mantic classes, such as color, size, or national- ity, and specify a sequential order among the classes. However, it is not …

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