Traditionally, coreference resolution is done by mining the reference relationships between NP pairs. However, an individual NP usually lacks adequate description information of its referred entity. In this paper, we propose a supervised learning-based approach which does coreference resolution by exploring the relationships between NPs and coreferential clusters. Compared with individual NPs, coreferential clusters could provide richer information of the entities for better rules learning and reference determination. The evaluation done on MEDLINE data set shows that our approach outperforms the baseline NP-NP based approach in both recall and precision.
REF test a supervised learning-based approach for anaphora resolution, evaluating it on MEDLINE abstracts from the GENIA corpus.
An NP-Cluster Based Approach To Coreference Resolution
International Conference On Computational Linguistics
Computer Science