AI

Resolving Discourse-Deictic Pronouns: A Two-Stage Approach to Do It

Abstract

Discourse deixis is a linguistic phenomenon in which pronouns have verbal or clausal, rather than nominal, antecedents. Studies have estimated that between 5% and 10% of pronouns in non-conversational data are discourse deictic. However, current coreference resolution systems ignore this phenomenon. This paper presents an automatic system for the detection and resolution of discourse-deictic pronouns. We introduce a two-step approach that first recognizes instances of discourse-deictic pronouns, and then resolves them to their verbal antecedent. Both components rely on linguistically motivated features. We evaluate the components in isolation and in combination with two state-of-the-art coreference resolvers. Results show that our system outperforms several baselines, including the only comparable discourse deixis system, and leads to small but statistically significant improvements over the full coreference resolution systems. An error analysis lays bare the need for a less strict evaluation of this task.