A Probabilistic Genre-independent Model of Pronominalization

Our aim in this paper is to identify genreindependent factors that influence the decision to pronominalize. Results based on the annotation of twelve texts from four genres show that only a few factors have a strong influence on pronominalization across genres, i.e. distance from last mention, agreement, and form of the antecedent. Finally, we describe a probabilistic model of pronominalization derived from our data. 1 Introduction Generating adequate referring expressions is an active research topic in Natural Language Generation. Adequate referring expressions are those that enable the user to quickly and unambiguously identify the discourse entity that the expression co-specifies with. In this paper, we concentrate on an important aspect of that question, which has received less attention than the question of anaphora resolution in discourse interpretation, i.e., when is it feasible to pronominalize? Our aim is to identify the central factors that influence pronominalization acro..