We propose an algorithm for coreference resolution based on analogy with shift-reduce parsing. By reconceptualising the task in this way, we unite ranking- and cluster-based approaches to coreference resolution, which have until now been largely orthogonal. Additionally, our framework naturally lends itself to rich discourse modelling, which we use to define a series of psycholinguistically motivated features. We achieve CoNLL scores of 63.33 and 62.91 on the CoNLL-2012 DEV and TEST splits of the OntoNotes 5 corpus, beating the publicly available state of the art systems. These results are also competitive with the best reported research systems despite our system having low memory requirements and a simpler model.
['Kellie Webster', 'James R. Curran']
Limited memory incremental coreference resolution