this paper methods for constructing statistical models for computing the probability of attachment decisions. These models could be then integrated into scoring the probability of an overall parse. We present our methods in the context of prepositional phrase (PP) attachment.

A parser for natural language must often choose between two or more equally grammatical parses for the same sentence. Often the correct parse can be determined from the lexical properties of certain key words or from the context in which

this paper methods for constructing statistical models for computing the probability of attachment decisions. These models could be then integrated into scoring the probability of an overall parse. We present our methods in the context of prepositional phrase (PP) attachment. Earlier work [11] on PP-attachment for verb phrases (whether the PP attaches to the preceding noun phrase or to the verb phrase) used statistics on co-occurences of two bigrams: the main verb ( ) and preposition ( ) bigram and the main noun in the object noun phrase ( 1 ) and preposition bigram. In this paper, we explore the use of more features to help in modeling the distribution of the binary PP-attachment decision. We also describe a search procedure for selecting a "good" subset of features from a much larger pool of features for PP-attachment. Obviously, the feature search cannot be Jeff Reynar, from University of Pennsylvania, worked on this project as a summer student at I.B.M