Abstract. This paper is concerned with the task of preposition generation in the context of a grammar checker. Relevant features for this task can range from lexical features, such as words and their part-ofspeech tags in the vicinity of the preposition, to syntactic features that take into account the attachment site of the prepositional phrase (PP), as well as its argument/adjunct distinction. We compare the performance of these different kinds of features in a memory-based learning framework. Experiments show that using PP attachment information can improve preposition generation accuracy on Wall Street Journal texts.