The Mention-Pair Model

This chapter introduces one of the early and most influential machine learning approaches to coreference resolution, the mention-pair model. Initiated in the mid-1990s and further developed into a more generic resolver by Soon et al. in 2001 and many others, the simple model still remains a popular benchmark in the learning-based resolution research. The mention-pair model recasts the coreference resolution problem as a classification task in which a classifier is trained to decide for a given pair of noun phrases whether they corefer or not. In a second step, full coreference chains are built by clustering these pairwise decisions. This chapter reviews the main building blocks of the mention-pair model: the construction of positive and negative instances and the related problem of data set skewness, the selection of informative features, and the choice of machine learner and clustering mechanism.

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