_:nf21f360cae1842f69ad6305c432220fbxb0
<http://schema.org/description>
"People are often entities of interest in tasks such as search and information\nextraction. In these tasks, the goal is to find as much information as possible\nabout people specified by their name. However in text, some of the references\nto people are by pronouns (she, his) or generic descriptions (the professor,\nthe German chancellor). It is therefore important that coreference resolution\nsystems are able to link these different types of mentions to the correct\nperson name. Here, we evaluate two state of the art coreference resolution\nsystems on the subtask of Named Person Coreference, in which we are interested\nin identifying a person mentioned by name, along with all other mentions of the\nperson, by pronoun or generic noun phrase. Our analysis reveals that standard\ncoreference metrics do not reflect adequately the requirements in this task:\nthey do not penalize systems for not identifying any mentions by name and they\nreward systems even if systems find correctly mentions to the same entity but\nfail to link these to a proper name (she--the student---no name). We introduce\nnew metrics for evaluating named person coreference that address these\ndiscrepancies. We present a simple rule-based named entity recognition driven\nsystem, which outperforms the current state-of-the-art systems on these\ntask-specific metrics and performs on par with them on traditional coreference\nevaluations. Finally, we present similar evaluation for coreference resolution\nof other named entities and show that the rule-based approach is effective only\nfor person named coreference, not other named entity types."
<https://deepai.org/publication/named-person-coreference-in-english-news>